DEV Community: Gnana

How AI Is Reshaping the Data Engineer Role in 2026

Gnana — Fri, 29 May 2026 02:47:57 +0000

What Changed in Data Engineer Job Descriptions Around 2023?

For years, a Data Engineer job description was a known quantity: Python for pipeline code, SQL for transformations, Airflow for orchestration, Spark for batch processing, one cloud (AWS or Azure or GCP), and a warehouse. The role was about moving data reliably from sources to destinations that analysts could query. Machine learning was someone else's problem downstream.

That description still fits most postings today. But about 4 in 10 active Data Engineer postings now mention some form of AI, and a new vocabulary has appeared in the ones that do: vector databases, retrieval-augmented generation (RAG), LLM-integrated pipelines, AI agents. We analyzed every active Data Engineer posting on the InterviewStack.io job board as of May 2026, 6,736 listings, to map where that shift is and where it is not.

The short version: there are two stories happening at once. One is explicit and visible in posting text. The other is ambient, nearly invisible to job-description scanning, and much larger.

Key Findings

6,736 active Data Engineer postings analyzed across the live job board as of May 2026.

39.5% of postings (2,664 of 6,736) mention some form of AI, including traditional ML.

17.4% explicitly require new-wave generative AI skills such as LLMs, RAG, AI Agents, and vector databases: 1,169 postings.

$18,965 salary premium for US-based roles with new-wave AI requirements: median $136,520 vs. $117,555 for non-AI roles.

Machine Learning leads all AI skills at 30.6% of postings; LLMs (6.7%), AI Agents (6.6%), and RAG (4.5%) head the new-wave tier.

Healthcare leads all industries in explicit AI adoption at 27.9%, ahead of technology (22.4%) and software (21.5%).

Senior roles show 18.3% AI adoption vs. 12.3% for entry-level: AI infrastructure is senior-level work.

72% of data practitioners use AI coding tools daily (dbt Labs 2026, n=363), a figure more than 4x the explicit posting rate.

What Did the Data Engineer Role Look Like Before the AI Era?

In 2021 and 2022, the role's core was pipeline reliability: ingest data from sources, transform it with Python and SQL, load it into a warehouse analysts could query, keep it running. The modern data stack (Snowflake, dbt, Databricks, Airflow) was exciting and rapidly being adopted. Machine learning was present in roughly 1 in 5 postings, mostly as a supporting concern: build feature stores for the ML team, ensure clean data reaches model training jobs. Model development was someone else's job.

AI coding tools barely existed. ChatGPT launched in November 2022; GitHub Copilot had limited early access through mid-2022 with minimal adoption. The standard development workflow was Stack Overflow, documentation, and tribal knowledge. "Uses AI tools" was not a skill; it was not even a concept.

That baseline matters because the ambient shift since 2022 dwarfs the explicit one. By 2025, the JetBrains State of Developer Ecosystem survey (n=24,534) found 85% of developers using AI tools regularly and 62% using at least one AI coding assistant. The Stack Overflow 2025 Developer Survey put daily AI tool use among professional developers at 51%. For data practitioners specifically, dbt Labs' 2026 State of Analytics Engineering report (n=363) found 72% now use AI-assisted coding as their primary productivity pattern, and 77% of data team leaders cite AI as essential for productivity gains.

None of those behaviors appear in job descriptions, the same way "uses Google" never appeared in 2005 job ads despite being universal. That is the ambient layer. The 17.4% explicit figure from today's postings measures companies that want Data Engineers to build AI systems. The 72% ambient figure measures companies whose engineers already use AI to build everything else.

What Are Companies Explicitly Requiring from Data Engineers Today?

The explicit AI layer is measurable directly from posting text across 6,736 active postings:

Share of Data Engineer postings by AI skill category, May 2026. "New-wave AI" covers generative AI skills from 2023 onward (LLMs, RAG, vector databases, AI agents). "Traditional ML" covers Machine Learning, Deep Learning, and MLOps.

No AI skills at all: 60.5% of postings (4,072 of 6,736)
Traditional ML only: 21.4% (machine learning, deep learning, and MLOps without any gen AI)
New-wave generative AI only: 8.1% (546 postings with gen AI skills but no traditional ML)
Both generative AI and traditional ML: 9.2% (623 postings)

Sixty percent of postings still describe a role that looks essentially identical to the 2020 version of the job. The 40% that do mention AI split between traditional ML (a presence that dates to long before 2023) and new-wave generative AI (the genuinely new signal).

The correct mental model for candidates: "17.4% of Data Engineer postings require you to build AI systems. Virtually all of them expect you to use AI tools to do your work." That gap is not a paradox. It is the difference between AI as the product you ship and AI as the shovel you use to build it.

Which AI Skills Are Reshaping the Data Engineer Role?

Top AI skills in active Data Engineer postings as a percentage of all 6,736 postings analyzed.

The skill list splits cleanly by vintage:

Traditional AI (present in postings for five-plus years):
Machine Learning leads at 30.6% (2,061 postings), the long-running catch-all for "can work with models and support ML pipelines." MLOps (the discipline of keeping production ML models healthy in production, not just building them) follows at 7.4% (498 postings). Deep Learning is at 2.3% (152), mostly in research-adjacent and computer-vision pipeline roles.

New-wave generative AI (2023 onward):
LLMs top this tier at 6.7% (450 postings), covering both consuming LLM APIs and building the infrastructure around them. AI Agents follow closely at 6.6% (446), reflecting demand for engineers who can build agentic data-retrieval and pipeline-monitoring systems. RAG appears in 4.5% of postings (301). This is the most data-engineering-specific new-wave skill on the list: building the embedding pipelines and vector stores that make RAG work is exactly the plumbing work Data Engineers are built to own. Vector Databases (the storage layer for RAG and semantic search) appear in 3.5% of postings (235).

Further down, LangChain (a Python framework for composing LLM-powered pipelines) appears in 1.9% of postings (127), LangGraph (LangChain's extension for multi-agent graph-based pipelines) in 1.1% (75), and GitHub Copilot in 1.0% (69), one of the rare ambient tools that surfaces explicitly when a company has standardized on it.

The pattern connecting these skills: Data Engineers are being asked to build the plumbing for AI systems. The underlying work looks familiar (pipeline design, data modeling, orchestration) but the targets are new. Instead of moving rows into warehouses, the job increasingly involves moving embeddings into vector stores, routing model outputs through evaluation pipelines, and tracing agent interactions through observability platforms. If you already understand how to build reliable, observable data infrastructure, the new-wave skills are a layer on top, not a replacement for what you know.

How Much More Do Data Engineers With AI Skills Earn?

Among US postings with disclosed salary data, the AI premium is significant. The numbers below are US base salary only: equity, RSUs, bonuses, and sign-on are not included in posted compensation data, so total compensation at top employers is meaningfully higher than what follows.

Median US base salary for Data Engineer postings split by AI requirement. US postings with structured salary disclosure only.

Postings that explicitly require new-wave AI skills show a median base salary of $136,520 (n=206), compared with $117,555 (n=447) for postings without any AI requirements. That is a premium of $18,965, roughly 16% above the non-AI baseline.

Some of that premium reflects seniority: AI-intensive roles skew toward senior levels, and senior roles pay more regardless of AI requirements. But a 16% premium is large enough that the AI skill itself carries real compensation weight. A mid-level engineer who adds a working RAG implementation or a vector database project to their portfolio moves from the $117K range of conventional postings into the $136K range of AI-intensive ones.

Who Is Leading the AI Shift in Data Engineering?

Does seniority predict AI requirements?

Percentage of Data Engineer postings at each seniority level that include AI skill requirements.

Senior-level postings carry the highest AI adoption rate at 18.3% (859 of 4,696 senior postings), compared with 12.3% for entry-level (13 of 106). The pattern makes sense: designing vector database schemas, architecting RAG pipelines, and building multi-agent systems are decisions that land on senior engineers, not new hires maintaining existing ETL jobs.

For career planning, this means AI skills become more load-bearing as you level up. Junior engineers who build familiarity now, through portfolio projects or direct LLM API work, position themselves ahead of the curve as those requirements migrate downward into mid-level postings over the next few years.

Which industries are furthest ahead?

Percentage of Data Engineer postings in each industry that include AI skill requirements.

Healthcare leads at 27.9% (72 of 258 postings). That might seem counterintuitive until you consider the demand drivers: clinical AI applications require some of the most demanding data engineering in any industry. Regulatory-compliant patient data pipelines for LLM-assisted diagnostics, medical imaging preprocessing, and real-world evidence generation for drug trials all require specialized, auditable data infrastructure. Companies like IQVIA (45% of 87 postings require AI) and Veeva Systems (80% of 15 postings) are representative of this pattern.

Technology and software companies land at 22.4% and 21.5%, roughly what you would expect from Copilot-saturated, greenfield-AI-heavy sectors. Finance and insurance sit at 18.5% and 18.8%: the data intensity is there, but risk governance constraints slow AI adoption without preventing it. Worth noting: 71% of data practitioners now say they fear hallucinated or bad AI data reaching stakeholders (dbt Labs 2026). That concern is highest in regulated industries, which explains why finance and healthcare are investing heavily in engineers who understand AI pipelines deeply enough to build them with appropriate guardrails.

The laggard is IT services at 3.6% (7 of 193 postings). IT services firms primarily operate and maintain established systems on existing contracts; greenfield AI work tends to flow through consulting and software-services firms instead.

Which companies lead the hiring?

By AI adoption rate among companies with meaningful posting volume: AgileEngine leads at 64% of its 25 postings, Blend360 at 59% of 27, and Exadel at 56% of 81 postings. It is worth noting that all three are software outsourcing and nearshore engineering firms; their high AI rates reflect a business model built around placing AI-skilled engineers on client projects, rather than product-first AI development. In healthcare data, IQVIA has 39 AI-focused Data Engineer roles out of 87 total (45%). Larger consulting firms like Accenture post higher absolute volumes with more moderate AI rates (22 AI-intensive roles out of 280 total, around 8%), which in absolute terms still represents substantial open capacity for candidates with AI infrastructure skills.

How to Use This in Your Job Search

The two-layer picture has direct implications for preparation.

For the 60% of postings with no explicit AI requirements: Standard Data Engineer interview prep applies. SQL, Python, pipeline architecture, cloud infrastructure, data modeling. Practice with AI mock interviews to sharpen responses on pipeline design, orchestration trade-offs, and data quality scenarios. The InterviewStack.io question bank covers the SQL, system design, and data modeling topics that come up most in Data Engineer onsite rounds.

For the 17% with explicit AI requirements: You need a working knowledge of RAG architecture, vector database fundamentals, and at least one LLM orchestration framework such as LangChain. A portfolio project that demonstrates these concretely (a working RAG pipeline, an agent workflow, or a vector search implementation over a real dataset) is a stronger signal than listing the skills. Our interactive courses cover the foundations needed to build toward that kind of project.

Regardless of which tier you target: Use AI tools in your daily work now. When an interviewer asks how your workflow has changed, being able to describe specifically how you use Copilot or similar tools to accelerate SQL generation, debug pipeline errors, or scaffold boilerplate code is a meaningful signal. Informed, opinionated AI use (including knowing when it gets things wrong) reads better in an interview than no opinion at all.

Browse current Data Engineer openings on the InterviewStack.io job board, or filter by specific AI skills to find postings that currently include LLM requirements or RAG experience.

FAQ

Q. What percentage of Data Engineer jobs require AI skills in 2026?

About 39.5% of the 6,736 active Data Engineer postings analyzed in May 2026 mention some form of AI, including traditional ML. New-wave generative AI skills such as LLMs, RAG, and AI Agents appear explicitly in 17.4% of postings (1,169 of 6,736). That figure measures roles built around AI-powered data products. Survey data paints a different picture for ambient usage: 72% of data practitioners report using AI coding tools daily (dbt Labs 2026 survey), whether or not their job posting mentions it.

Q. How much more do Data Engineers with AI skills earn?

Among US postings with disclosed salary data, roles that explicitly require new-wave AI skills show a median base salary of $136,520 (n=206), compared with $117,555 (n=447) for postings without AI requirements. That is an $18,965 premium, roughly 16% above the non-AI baseline. These are US base salary figures only; equity, bonuses, and sign-on are not included.

Q. What specific AI skills are companies asking for in Data Engineer postings?

Machine Learning is the most-mentioned AI skill at 30.6% of postings (2,061 of 6,736), a figure present for years. Among new-wave skills, LLMs lead at 6.7% (450 postings), followed by AI Agents at 6.6% (446), Generative AI at 5.6% (380), and RAG at 4.5% (301). Vector Databases appear in 3.5% of postings, MLOps in 7.4%, and LangChain in 1.9%.

Q. Which industries are leading the AI shift in Data Engineering?

Healthcare leads with 27.9% of its Data Engineer postings explicitly requiring AI skills (72 of 258 postings), ahead of technology at 22.4% and software at 21.5%. IT services companies are the laggard at just 3.6% (7 of 193 postings). Healthcare's lead reflects demand for clinical AI pipelines, LLM-assisted diagnostics, and regulatory-compliant AI data infrastructure.

Q. Does a Data Engineer need AI skills to get hired in 2026?

For most roles today, AI is not yet an explicit gate: only 17.4% of postings list new-wave AI skills as a requirement. But ambient AI tool use is now an expected baseline. 72% of data practitioners use AI coding tools daily (dbt Labs 2026), and most employers assume that productivity without stating it. The practical read: traditional Data Engineer skills (Python, SQL, pipelines, cloud) still open the door; AI skills raise your offer by roughly $19K and make you competitive for a growing share of senior roles.

Q. Which companies are hiring the most AI-focused Data Engineers?

By AI adoption rate among companies with significant posting volume, the leaders include AgileEngine (64% of 25 postings require AI), Blend360 (59% of 27), Exadel (56% of 81), and IQVIA (45% of 87). The top three by adoption rate are software outsourcing and nearshore engineering firms whose high figures reflect demand for AI-skilled engineers on client projects. Healthcare and life sciences firms also rank highly: IQVIA (clinical data analytics) and Veeva Systems (80% of 15 postings, life sciences software) reflect demand for specialized, regulated-industry AI data infrastructure. Larger firms like Accenture post high absolute counts with moderate AI rates around 8%.

Final Thoughts

The Data Engineer role in 2026 is not being automated away. It is being redirected. The core pipeline skills that define the Data Engineer role are still the foundation: most postings still require Python, SQL, and cloud infrastructure expertise. What has changed is what those pipelines increasingly carry: embeddings flowing into vector databases, model outputs routed through evaluation pipelines, agent traces feeding observability platforms. The engineers who build that infrastructure fluently, and who use AI tools to build faster and debug smarter, are the ones landing the top-of-range offers. The floor has not moved much; the ceiling has. For a parallel view of the same shift in a neighboring role, see how AI is reshaping Software Engineering in 2026.

Software Engineer vs Frontend Developer: 2026 Comparison

Gnana — Thu, 28 May 2026 05:55:28 +0000

The Short Answer

Software Engineers earn a median $143,000 US base vs. $130,000 for Frontend Developers, a 10% premium. But salary is the secondary story. The primary story is volume: there are 32 active Software Engineer postings for every Frontend Developer posting on the InterviewStack.io job board as of May 2026. "Software Engineer" is a broad umbrella that swallows dozens of specializations, including frontend work. "Frontend Developer" is a precise title that captures one specific slice of it.

Key Findings

Software Engineer postings (32,310) outnumber Frontend Developer postings (1,019) by 31.7 to 1 on the InterviewStack.io job board as of May 2026.

Median US base salary: $143,000 for Software Engineers (n=8,296) vs. $130,000 for Frontend Developers (n=31; small sample, treat as directional only).

Skill overlap is low: a Jaccard coefficient of 0.22 means roughly 22% of the top-30 skill sets are shared between the two roles.

JavaScript and React are table stakes for Frontend Developers (65.5% and 58.1% of postings) but appear in fewer than 1 in 5 Software Engineer postings.

Python (36.2%), Java (27.1%), Kubernetes (18.7%), and Docker (18.4%) are effectively absent from Frontend Developer postings.

Entry-level openings are scarce in both roles: 3.5% for Software Engineer, 5.0% for Frontend Developer.

No AI skills appear in Frontend Developer postings' top-30 skill list; 51% of all professional developers use AI tools daily (Stack Overflow Developer Survey 2025), making AI tool fluency a baseline expectation even where postings don't mention it.

	Software Engineer	Frontend Developer
Median US base salary	$143,000	$130,000 (n=31)
Active postings (May 2026)	32,310	1,019
Top skill	Python (36.2%)	JavaScript (65.5%)
Remote share	19.7%	19.5%
Entry-level share	3.5%	5.0%

What Does Each Role Actually Do?

A Software Engineer builds systems. The title is deliberately broad: embedded firmware, mobile apps, backend APIs, developer tooling, security infrastructure, ML pipelines, and yes, frontend interfaces all live under this label. The common thread is owning a component that runs in production and staying accountable for its correctness and reliability over time. The exclusive skills confirm this: Python, Java, SQL, Kubernetes, Docker, and observability tooling all point toward code that runs somewhere other than a browser, at scale, under operational pressure.

A Frontend Developer builds what users see. The job is translating design into functional browser interfaces: React components, CSS layouts, responsive breakpoints, form interactions, and client-side state. The exclusive skills (CSS, HTML, Angular, Next.js, Redux, Jest) are entirely in the presentation and interactivity layer. The handoff is typically to QA or directly to end users, not to downstream engineering teams.

If the question is "which offers more specialization options over a career," Software Engineer wins by scope. If the question is "which is most focused on the product surface a user actually touches," Frontend Developer is the answer.

What Skills Do Both Roles Require?

Both roles share a core of modern web development: JavaScript, React, TypeScript, Agile, CI/CD, and API integration.

Top shared skills by frequency across Software Engineer (emerald) and Frontend Developer (blue) postings. Frequency gaps in JavaScript and React reflect how browser-layer work is table stakes for frontend but one specialization within the broader SE scope.

The "shared" label needs context. JavaScript appears in 65.5% of Frontend Developer postings but only 18.3% of Software Engineer postings. React: 58.1% vs. 19.0%. TypeScript: 48.9% vs. 21.7%. These skills clear the overlap threshold in both roles, but they signal different things: for a Frontend Developer, React and TypeScript are non-negotiable. For a Software Engineer, they mark a specific frontend or full-stack specialization among many possible paths.

One connection worth noting: per the GitHub Octoverse 2025 report, TypeScript overtook Python and JavaScript to become the most popular language on GitHub in late 2025, partly driven by AI coding tools. Strongly typed languages give AI assistants clearer constraints and produce more reliable generated code. Both roles are converging on TypeScript, and that convergence is accelerating as AI-assisted development becomes the default workflow.

The skills that genuinely overlap in frequency are Agile (Software Engineer 33.4%, Frontend Developer 33.0%) and CI/CD (29.4% vs. 23.0%). These transfer cleanly in either direction.

Where Do the Roles Diverge?

Skills exclusive to Software Engineer (high frequency in SE, rare or absent in FD):

Python: 36.2% of postings
Java: 27.1%
Automation: 24.6%
SQL: 20.2%
Kubernetes: 18.7%
Docker: 18.4%
Monitoring: 17.8%
Observability: 14.7%

This cluster reads as a backend and infrastructure portfolio. Python and Java signal general-purpose server-side work. SQL signals data access at the application layer. Kubernetes and Docker signal production container deployment. Monitoring and observability signal operational ownership: you are responsible not just for shipping code but for keeping it running. Companies posting these skills expect engineers who can debug a production incident, not just merge a PR.

Data note: Accenture (consulting/IT services) accounts for roughly 10.7% of Software Engineer postings in this dataset, the single largest employer in the sample. This modestly amplifies enterprise-common skills like Java and Agile relative to a purely product-company sample, though the overall skill distribution remains representative of the broad SE title.

Skills exclusive to Frontend Developer (high frequency in FD, rare or absent in SE):

CSS: 49.7% of postings
HTML: 37.0%
Angular: 32.7%
HTML5: 24.8%
CSS3: 20.1%
Jest: 17.5%
Redux: 17.1%
Next.js: 16.0%

The FD-exclusive list is entirely browser and UI layer. CSS and HTML appear in nearly half and more than a third of postings, meaning rendering and layout are non-negotiable, not background knowledge. Angular and Vue.js signal companies that have standardized on frameworks other than React. Jest signals that frontend testing is treated as a first-class discipline.

A note on AI skills: roughly 9.6% of Software Engineer postings explicitly mention LLM or generative AI skills (3,111 of 32,310 postings). Frontend Developer postings show virtually no explicit AI requirements in their top-30 skills. The distinction is meaningful: that ~10% of SE postings measures roles hired to build AI-powered systems, integrate LLM APIs, or operate ML pipelines. It does not measure ambient AI tool usage. The Stack Overflow Developer Survey 2025 found 84% of developers use or plan to use AI tools, and 51% use them daily, a floor that applies equally to frontend developers even though their job postings almost never state it. GitHub Copilot reached approximately 20 million users by mid-2025 and is present in 90% of Fortune 100 companies, meaning most engineers at major employers work in AI-augmented environments regardless of what their job posting says.

Which Pays More?

Among US postings with disclosed salary data (base salary only; equity, bonus, and sign-on are not included in job posting data and not in this dataset): Software Engineers earn a median $143,000 (n=8,296), compared to $130,000 for Frontend Developers (n=31). The $13,000 gap represents a 10% premium for the broader title.

Treat the Frontend Developer figure with care: 31 US salary disclosures is a very small sample, likely concentrated in states with wage-transparency laws. The Software Engineer figure, backed by 8,296 data points, is substantially more reliable as a market signal.

US base salary medians. Software Engineer salary by skill shown where n >= 25. Frontend Developer skill-level salary not shown (insufficient US disclosures for skill-level analysis).

Within Software Engineer, specializations that go deeper into systems-level or AI work command meaningful premiums above the $143,000 baseline. Distributed Systems postings show a median of $163,900 (+$21K, n=1,564). Machine Learning postings reach $157,000 (+$14K, n=921). LLM-related postings hit $153,000 (+$10K, n=504). These are the specializations that lift a generalist Software Engineer title toward the senior and staff compensation ceiling. Frontend-adjacent SE work sits much closer to the role baseline: React postings show a median of $146,600 (+$3,600 above baseline) and TypeScript postings $146,000 (+$3,000). Both are above baseline but well below the premium attached to systems and AI depth, reinforcing that the salary ceiling in the SE title correlates with how far from the browser layer the work sits.

Which Has More Job Openings?

Software Engineer postings (32,310) outnumber Frontend Developer postings (1,019) by 31.7 to 1. This is structural, not cyclical. "Software Engineer" absorbs a huge range of specializations. "Frontend Developer" is a precise label used by companies that explicitly differentiate frontend from the rest of their engineering org.

On entry: scarce openings in both markets. 3.5% of Software Engineer postings are explicitly entry-level (roughly 1,130 postings), and 5.0% of Frontend Developer postings are entry-level (roughly 51 postings). Mid-level dominates both: 48.2% of SE and 58.8% of FD postings target mid-level candidates. Staff-level openings are notably larger in SE (15.3%) than FD (4.1%), reflecting the broader career ladder the SE title supports.

Work mode is nearly identical across both roles: around 19-20% remote, 24-27% hybrid, and about 51% onsite. Neither role offers a meaningful remote advantage over the other.

Geography diverges. The US is the largest market for Software Engineers at 40% of postings, with India second at 23%. Frontend Developer postings are more internationally distributed: the largest markets are unknown/unspecified (15.7%), India (11.9%), and the US (10.3%), followed by Germany (4.3%), Canada (3.6%), and Portugal (3.1%). If you are outside the US, the Frontend Developer title may actually offer better geographic reach. That said, treat this distribution with some caution: the top Frontend Developer employers in this dataset are predominantly staffing and IT services firms: PradeepIT, Boardroom Appointments, Nexthire, HawodTech Solutions, ADI Recruitment, and Datamatics Technologies collectively appear among the top fifteen employers. A significant portion of FD openings likely represent contract or agency placements rather than direct-hire product roles; this context partly explains the broad international distribution and the very low US salary disclosure rate (n=31).

Which Should You Choose?

Choose Software Engineer if you:

Want flexibility to specialize over time (backend, infra, ML, security, or frontend) without switching titles
Have or plan to build a Python, Java, or backend-heavy foundation
Want the largest absolute number of job opportunities and the widest company range to target
Are interested in systems-level work, operational ownership, or AI-powered product development

Choose Frontend Developer if you:

Know you want to work in the browser and care specifically about UI, design systems, and user-facing product quality
Already have or are building fluency in CSS, HTML, and a major framework (React, Angular, or Vue.js)
Are comfortable targeting a smaller but focused hiring market where browser-layer depth is the primary signal
Are outside the US and want a title with broader international distribution

One practical note: many Software Engineer postings are implicitly or explicitly frontend-focused. React (19%) and TypeScript (21.7%) are among the top Software Engineer skills. If you want frontend work but want the larger job pool, searching "Software Engineer" and filtering by React or TypeScript surfaces a substantial slice of the same market without the title restriction.

Bottom Line

Software Engineer and Frontend Developer share a JavaScript and TypeScript surface, then diverge sharply: SE covers backend, infrastructure, and AI system-building; FD focuses on the browser layer. The salary gap is a modest 10%, though the SE figure is far more statistically reliable. The volume gap is not modest: 32 SE postings exist for every FD posting on the board.

Whichever path you choose, targeted practice is what converts market knowledge into offers. Use AI mock interviews to drill the specific role's technical and behavioral questions, and the Question Bank to work through the skill areas where postings show the most demand. Browse live Software Engineer openings and Frontend Developer openings on InterviewStack.io to see where the market stands today.

FAQ

Q. What is the median salary difference between Software Engineer and Frontend Developer in 2026?

Software Engineers earn a median US base salary of $143,000 (n=8,296 US postings with disclosed salary), vs. $130,000 for Frontend Developers (n=31 US postings). The $13,000 gap carries a caveat: the Frontend Developer sample is very small and may not be representative of the full market. Neither figure includes equity or bonuses.

Q. What skills do Software Engineers and Frontend Developers share?

Both roles commonly require JavaScript, React, TypeScript, Agile, CI/CD, and API integration. In Frontend Developer postings, JavaScript reaches 65.5% and React 58.1%, compared to 18.3% and 19.0% respectively in Software Engineer postings, where these are one slice of a much broader stack.

Q. Which is easier to break into: Software Engineer or Frontend Developer?

Frontend Developer roles have a slightly higher entry-level share (5.0% of postings) vs. Software Engineer (3.5%). But Software Engineer has 32 times more total postings (32,310 vs 1,019), so in absolute terms there are far more entry-level SE openings available. Both are competitive; the SE market simply offers more volume.

Q. What makes Frontend Developer skills different from Software Engineer skills?

Frontend Developer postings center on browser-layer work: CSS (49.7%), HTML (37.0%), Angular (32.7%), HTML5 (24.8%), CSS3 (20.1%), and Next.js (16.0%). Software Engineer postings emphasize backend and infrastructure: Python (36.2%), Java (27.1%), Kubernetes (18.7%), Docker (18.4%), and distributed-systems observability. Their Jaccard skill overlap is 0.22, meaning only about 22% of skills overlap.

Q. Which role has more job openings in 2026?

Software Engineer postings outnumber Frontend Developer postings 31.7 to 1 on the InterviewStack.io job board as of May 2026: 32,310 active SE postings vs. 1,019 FD postings. "Software Engineer" is the highest-volume engineering title on the board; "Frontend Developer" captures only postings using that specific label.

Q. Do Frontend Developers need AI skills in 2026?

No AI skills appear in the top-30 Frontend Developer skill list, but that measures roles hired to build AI systems, not ambient tool usage. The Stack Overflow Developer Survey 2025 found 51% of professional developers use AI tools daily, and GitHub Copilot reached roughly 20 million users by mid-2025, present in 90% of Fortune 100 companies. AI tool fluency is now a baseline expectation for frontend developers, even when job postings don't say so.

How AI Is Changing the QA Engineer Role in 2026: A Data Analysis

Gnana — Fri, 22 May 2026 01:02:10 +0000

How Has the QA Engineer Job Description Changed in 2026?

Open a QA Engineer job posting from 2022 and you will find a familiar checklist: Selenium or Cypress, a primary scripting language (usually Python, Java, or JavaScript), test framework experience (JUnit, TestNG, pytest), a CI/CD pipeline, an API testing tool like Postman, and behavior-driven development (BDD) for the more progressive shops. Open one from May 2026 and that checklist is still there, but a new layer has begun to settle on top: test the AI features the product team just shipped, build evaluation harnesses for LLM outputs, and use AI assistants to author and maintain the test suite itself.

To put numbers on it, we looked at every active QA Engineer posting on the InterviewStack.io job board over the trailing 90 days as of May 2026, 16,376 listings, with AI skills extracted from descriptions and synonyms collapsed (so "ChatGPT", "OpenAI API", and "Anthropic Claude" each get counted under the right canonical concept).

The headline: AI is not yet pervasive in QA hiring (4.3% of postings explicitly mention new-wave generative AI), but the salary premium for AI-fluent QA Engineers is the largest we have measured across our AI-shift analyses to date (including Software Engineering), roughly 45% over the non-AI baseline. That gap signals scarcity, and scarcity is what early movers convert into offers.

Key Findings

16,376 active QA Engineer postings analyzed across the live job board in May 2026.

4.3% of postings explicitly mention new-wave generative AI skills (712 of 16,376). When traditional machine learning is included, the share rises to 6.1% (998 postings).

Median US base salary is $119,000 for postings that ask for new-wave AI skills (n=75), versus $82,041 for postings without AI (n=3,264). That is a $36,959 premium, or roughly 45% above the non-AI baseline.

Machine Learning is still the top AI-adjacent skill in QA postings at 2.8% (458 listings); LLMs (1.4%) and AI Agents (1.4%) are the leading new-wave entries.

Staff QA Engineers see the highest AI demand at 7.3%; senior follows at 4.8%, mid-level at 3.6%, junior at just 1.5%. AI work is concentrated at the experienced end of the ladder.

Professional services leads industries at 31.3% AI adoption among its QA Engineer postings, more than double any other sector.

Poland (18.1%), Mexico (10.0%), and India (9.9%) all post AI-related QA roles at a higher rate than the US (2.1%), reflecting consulting-led offshore work on AI testing engagements.

What Did the QA Engineer Role Look Like in 2022?

Three or four years ago, the QA Engineer description was one of the more stable job specs in software. The Stack Overflow Developer Survey 2022 captured the developer-side baseline well: Selenium and Cypress as the dominant browser-test frameworks, Jest as the rising JavaScript test runner, Postman for API testing, and JUnit / TestNG / pytest for unit and integration layers. The GitHub Octoverse 2022 report tracked the same migration toward open-source test stacks (Playwright grew sharply that year) and toward GitHub Actions as the default CI integration point.

A typical 2022 QA Engineer posting asked for some combination of: a primary scripting language (Python, Java, or JavaScript), one browser automation framework (Selenium or Cypress), a unit-test framework, API testing tools, CI/CD integration (Jenkins or GitHub Actions), JIRA, and either Agile/Scrum or a structured test-management tool. Manual testing was already being phased out as a stand-alone discipline; "QA Engineer" had largely consolidated around test automation. Generative AI was not in the working vocabulary: ChatGPT had only just launched in November 2022, and no hiring manager was writing "Prompt Engineering" into a QA job description.

The result was that "QA Engineer" was narrow and stable across the industry. A posting from a fintech and a posting from a healthcare SaaS company would look almost interchangeable at the top of the spec. That stability is what is now starting to crack.

What Share of QA Engineer Postings Now Ask for AI Skills?

The first thing the 2026 data shows is that AI has reached QA, but only at the margins. The growth is visible, the salary signal is loud, but the absolute share of QA postings asking for AI is much smaller than in Software Engineering.

Of 16,376 active QA Engineer postings, 6.1% mention some form of AI. New-wave generative AI (the LLM-era stack of Agents, Prompt Engineering, RAG, GitHub Copilot) appears in 4.3%, with 1.2% of postings asking for both new-wave AI and traditional ML.

Three things to pull out of that chart. First, the new-wave generative AI cohort (3.1% on its own, 4.3% including overlap) is already larger than the pure traditional-ML cohort (1.7%). In just over three years since ChatGPT launched, the LLM-era toolkit has overtaken classical ML as a QA hiring requirement, even though both are small. Second, only 1.2% of postings ask for both new-wave AI and traditional ML, almost always senior or staff roles at companies running production ML platforms that now also have generative AI features layered on top. Third, 93.9% of postings still ask for neither. Regression testing, browser automation, API testing, and manual test-case work still drive the vast majority of QA hiring. AI is the leading edge, not the median.

For a QA Engineer deciding whether to invest in AI skills now or wait, the "any AI" share (6.1%) understates how concentrated that demand is. As we will see in the salary and seniority cuts below, the AI-asking postings are clustered at the senior tier and pay a steep premium, which means they screen a much smaller pool of candidates.

Which AI Skills Are Reshaping the QA Engineer Role?

Drill into the AI skills that show up across QA postings and a clear two-track story emerges. The top of the list is dominated by what QA Engineers are being asked to test against (LLMs, AI Agents, Generative AI). The next tier is dominated by what QA Engineers are being asked to use (GitHub Copilot, ChatGPT, AI-assisted development).

Share of QA Engineer postings that mention each AI skill. Traditional ML (gray) has been in QA postings for years; the new-wave generative AI stack (highlighted) is what changed since 2023.

The ranking tells a clear story:

Machine Learning (2.8%, 458 postings) is the largest single AI-related skill. Most of those are not new-wave generative AI: they are postings at companies running production ML pipelines (recommender systems, fraud models, demand forecasting) where the QA team validates training data, monitors model drift, and writes acceptance tests against ML outputs. This is not a 2026 phenomenon; it has been at roughly this level for several years.
LLMs (1.4%, 234) and AI Agents (1.4%, 222) are the leading new-wave entries. Postings in this band ask QA Engineers to test against LLM-powered features: chat interfaces, summarization endpoints, agentic workflows that call tools and make decisions. The work pulls in evaluation-harness design, prompt-based regression suites, and accuracy/hallucination scoring. (Browse QA Engineer roles asking for LLMs.)
Generative AI (1.2%) and Prompt Engineering (0.5%) form the second new-wave tier. Prompt Engineering specifically is interesting in a QA context: it appears as a testing technique (varying prompts to probe failure modes) more often than as a development skill. RAG (retrieval-augmented generation, where an LLM is grounded against a vector store) and OpenAI (0.4%) round out the application stack.
AI-Assisted Development (0.6%, 91), GitHub Copilot (0.5%, 87), and ChatGPT (0.5%, 84) form a distinct cluster. These postings are not asking the QA Engineer to test AI products; they are asking the QA Engineer to use AI assistants to write tests, draft test plans, and generate test data faster. It is now an explicit hiring signal at a small but visible fraction of employers. (QA Engineer + GitHub Copilot openings.)
LangChain (54), RAG (49), Vector Databases (16), and Anthropic / Claude (10) make up the long tail of framework-specific mentions. Specific framework brands stay niche; the volume sits on the underlying concepts (LLMs, Agents, RAG) rather than on tool names, which is what you would expect in a stack that is still settling.

The clearest signal across the ranking: AI is reaching QA on two distinct vectors at once, and the postings that pull both vectors together (test AI features and use AI to test) are the highest-paying and most senior. The next section quantifies the pay side directly.

How Much Does AI Knowledge Raise a QA Engineer's Salary?

Among US postings (where wage-transparency laws produce consistent disclosure), the median QA Engineer base salary in postings that do not require AI skills is $82,041 (n=3,264). In postings that require new-wave generative AI skills, the median jumps to $119,000 (n=75), a $36,959 difference, or roughly 45% above the non-AI baseline. Equity, bonus, and sign-on are not disclosed in postings, so total compensation at AI-heavy employers (companies building agentic platforms, applied-AI startups, frontier labs) runs materially higher than these base figures suggest.

Median US base salary for QA Engineer postings with and without new-wave AI skill requirements. Equity and bonus are not in this dataset.

That premium is unusually steep. For context, our analysis of Software Engineer roles found a $21,000 (16.2%) premium for AI skills. QA Engineers see a premium more than 1.7 times larger in absolute dollars and nearly three times larger in percentage terms. The cleanest read of that gap is that AI-fluent QA Engineers are scarce relative to demand. Most QA candidates today are still optimizing for browser automation and API regression suites; the small group that has also built evaluation harnesses for LLM outputs or trained on production agentic workflows can name a number.

Two caveats are worth flagging. First, the AI-skill sample (n=75 US postings with disclosed salary) is much smaller than the no-AI sample (n=3,264). The point estimate of $119,000 has a wider band than the baseline. Second, the premium compounds with seniority: as the next section shows, AI demand clusters in the senior and staff tiers, which already pay more than the role average. Part of the $36,959 gap reflects seniority skew, not AI alone. Even so, the signal is unambiguous: AI knowledge is currently the single highest-paying skill class to add to a QA Engineer resume.

For a mid-level QA Engineer deciding whether to spend a quarter learning LLM evaluation, prompt-based testing, and one AI coding assistant, the payback is short. Targeting QA Engineer roles that ask for AI skills on your next job search compresses the salary jump even further.

Who Is Leading the Shift: Which QA Engineers, Industries, and Companies?

The shift is not happening evenly. Three cuts of the data make that obvious: by seniority, by industry, and by employer.

Share of postings at each seniority level that ask for AI skills. Staff and senior are the clear leaders.

The seniority pattern is monotonic at the top and tells a coherent story:

Staff QA Engineers (7.3%, 39 of 535) are the most-asked group. Companies need experienced QA leaders to design the evaluation harnesses, the AI-feature test strategies, and the data-quality gates that production LLM systems require. This is where the senior skills gap shows up most sharply.
Senior (4.8%, 487 of 10,123) is where the bulk of the absolute AI volume sits. The senior tier alone accounts for 61.8% of all QA Engineer postings, so even at a 4.8% adoption rate, it produces the largest cohort of AI-asking listings.
Mid-level (3.6%) and Entry-level (3.6%) are tied, but with very different stakes: entry-level postings are tiny in absolute count (506) and only 18 of them mention AI.
Junior (1.5%, 15 of 1,003) is the lowest cohort. The pattern is clear: companies are not pushing AI requirements onto early-career QA hires. They are pulling experienced testers up the stack to lead AI work.

The industry view amplifies the same point.

Share of QA Engineer postings within each industry that require AI skills. Professional services is the clear outlier.

Professional services (31.3%, 45 of 144) is a striking outlier, more than double the next group. These are the consulting and software-services firms staffing AI testing engagements at client companies that have not yet built the capability in-house. If you want exposure to a wide range of AI testing problems quickly, professional services is the fastest on-ramp; the trade-off is the project-rotation cadence rather than long-term ownership.
Technology (12.3%, 149) and Pharmaceutical (8.6%, 9) follow. Pharma's appearance here is worth noting: it sits above software (7.7%) on AI adoption rate even with a much smaller posting base, reflecting how aggressively drug-discovery and lab-automation teams are layering AI on top of their existing validation work.
Software (7.7%) and Finance (7.3%) round out the leaders. Finance is using LLMs for document review, compliance triage, and customer-support automation, all of which need QA against hallucination and accuracy.
Manufacturing (2.3%), education (2.5%), and consulting (2.6%) trail at the bottom. If your current role is in those sectors, the AI signal in the job market is genuinely weaker; switching industries may matter more than switching companies.

Geography tells a parallel story that surprises most readers. The United States is the largest single QA market by volume (6,659 postings, 41% of the dataset), but its AI adoption rate is only 2.1%, well below the global average. Poland leads at 18.1% (37 of 204 postings), followed by Mexico at 10.0% (35 of 350) and India at 9.9% (139 of 1,399). Most of that international volume flows through global capability centers and software-services firms (the same firms driving the professional-services number above) serving US and UK clients on AI testing engagements, which is why the consulting-led offshore markets are running ahead of the domestic US one.

On the employer side, the highest-density hirers are revealing. Nebius Academy (34 AI postings out of 44 QA Engineer roles, 77% AI density) and AgileEngine (33 of 33, 100%) are the most aggressive volume hirers. Several firms post at 100% AI density across smaller samples, including Marvell Technology (18 of 18), VML (8 of 8), Centific (8 of 8), Hyland (8 of 8), and Faro Health (6 of 6). Among large-cap technology employers, NVIDIA Corporation (7 of 40 QA postings, 17.5%), Waymo (6 of 20, 30%), and Cisco Systems (6 of 28, 21%) are the most AI-heavy. PricewaterhouseCoopers (8 of 27, 30%) is the highest-density Big Four name in QA, and Royal Bank of Canada (7 of 21, 33%) is the leading financial-services employer. If you want to optimize for AI exposure in your next QA role, those second-cluster firms (and the AI-native consultancies above) are the obvious targets.

How Should QA Engineers Use This Data in Their Job Search?

Three things follow from the data that you can act on this quarter.

First, treat AI as an extension of your QA stack, not a separate career path. The data shows AI requirements appearing inside generalist QA Engineer postings, not just inside AI-specialist QA roles. That means a tester who adds LLM evaluation, prompt-based regression design, and one AI coding assistant is competing for higher-paying generalist roles, not switching tracks. Practice the systems side of AI testing (eval harnesses, hallucination scoring, agent trace validation) the same way you practiced API regression suites a few years ago. Our AI mock interviews include scenarios for designing tests against LLM features, agentic workflows, and RAG pipelines, with feedback calibrated to what hiring managers at AI-heavy companies are actually asking.

Second, drill the specific concepts that recur in the postings. LLM evaluation, AI Agent test design, prompt engineering as a testing technique, vector-retrieval verification, and AI-assisted test authoring come up across the AI-asking QA postings. The Question Bank groups questions by topic so you can drill these one at a time rather than wandering through generic interview prep. Pair the conceptual drilling with one or two real projects on GitHub: an open-source eval harness for an LLM feature, or a test suite that uses an AI assistant to maintain itself. Recruiters at AI-heavy QA employers value "built with" over "read about", and a portfolio link compresses the screen.

Third, filter your search and follow the geography. Generic "QA Engineer" feeds will be 95.7% non-AI roles. If your goal is to find the AI premium, search the job board with AI-skill filters explicitly: QA Engineer + LLMs and QA Engineer + GitHub Copilot are good starting points. If you are open to remote-friendly consulting work, the full QA Engineer feed on InterviewStack.io is the right place to scan global capability-center listings, which currently lead the AI adoption rate by a wide margin. For company-specific interview structures, the preparation guides index covers the consulting and tech-firm processes that show up most often in AI-leaning QA hiring.

FAQ

Q. How is AI changing the QA Engineer role in 2026?

AI now appears in 6.1% of all QA Engineer postings (998 of 16,376 analyzed) and 4.3% specifically mention new-wave generative AI tools like LLMs, AI Agents, and GitHub Copilot. The shift is happening on two fronts: postings that ask QA Engineers to test AI-powered products, and postings that expect QA Engineers to use AI assistants to write and maintain tests. Adoption is still well below Software Engineering, but the US salary premium (about 45% over the non-AI baseline) is the largest we have measured across our AI-shift analyses to date, including Software Engineering.

Q. What is the salary premium for QA Engineers with AI skills?

Among US postings with disclosed base salary, QA Engineers with new-wave AI skills earn a median $119,000 (n=75), versus $82,041 (n=3,264) for postings without AI. That is a $36,959 premium, or roughly 45% above the non-AI baseline. Equity and bonus are not disclosed in postings, so total compensation at top employers runs higher than these numbers.

Q. Which AI skills appear most often in QA Engineer postings?

Machine Learning leads at 2.8% (458 postings), reflecting older test-automation work against ML systems. The new-wave generative AI stack follows: LLMs (1.4%, 234), AI Agents (1.4%, 222), Generative AI (1.2%, 189), AI-Assisted Development (0.6%, 91), GitHub Copilot (0.5%, 87), Prompt Engineering (0.5%, 86), and ChatGPT (0.5%, 84). Specific frameworks like LangChain (54) and RAG (49) sit further down the long tail.

Q. Are QA Engineer roles becoming entry-level AI jobs?

No. AI adoption is highest in Staff QA Engineer postings (7.3%, 39 of 535) and Senior postings (4.8%, 487 of 10,123). Junior postings sit at the bottom at 1.5%, and Entry-level matches Mid-level at 3.6%. The pattern is clear: companies want experienced QA Engineers to lead the AI work, not entry-level hires to learn it on the job.

Q. Which industries hire the most AI-aware QA Engineers?

Professional services leads dramatically at 31.3% AI adoption (45 of 144 postings), more than double the next group. Technology follows at 12.3% (149 of 1,212), then pharmaceutical (8.6%), software (7.7%), finance (7.3%), and healthtech (7.1%). The high professional-services number reflects consulting firms staffing AI testing engagements for client companies that have not yet built the capability in-house.

Q. Is the AI shift in QA bigger in some countries than others?

Yes, and the US is not in the lead. Poland posts AI-related QA roles at 18.1% (37 of 204), Mexico at 10.0% (35 of 350), and India at 9.9% (139 of 1,399), all well above the US rate of 2.1% (143 of 6,659). Most of that international volume flows through global capability centers and software-services firms serving US and UK clients, where AI testing engagements are concentrated.

Q. Should QA Engineers learn AI skills in 2026?

The salary data says yes if you are aiming for senior or staff roles. A $36,959 US base premium is large, and AI demand clusters in the senior tier (4.8%) and staff tier (7.3%) where the salary curve already steepens. Practical priorities: get fluent with one AI coding assistant for test authoring, understand LLMs and AI Agents well enough to design tests against them, and learn Prompt Engineering as a testing technique (it shows up in 86 postings already).

Final Thoughts

The QA Engineer title in 2026 is not yet being rewritten the way Software Engineer has been, but the leading edge is clearly visible. Roughly one in twenty QA postings already asks for some form of AI fluency, and the small group of QA Engineers who can credibly answer that ask is being paid a premium large enough that the rest of the field cannot ignore it for long. The testers who treat LLM evaluation and AI-assisted authoring as an extension of their existing automation discipline, rather than as a separate career, will track with where the role is heading next.

Software Engineer Skills Companies Want in 2026: 48K-Posting Analysis

Gnana — Thu, 21 May 2026 03:03:00 +0000

Why Does the Software Engineer Title Mean So Many Different Things in 2026?

If you scrape every "Software Engineer" posting on the open web in 2026, the most surprising finding is what's missing: a single skill that every job asks for. Python tops the list, but only at 34.8% of postings. Nothing else clears that. There is no skill in the Software Engineer market that plays the role SQL plays for Data Analysts or Python-plus-pipelines plays for Data Engineers, no universal filter the resume must clear.

That absence is the story. "Software Engineer" in 2026 is less a single role than a job-family label, and the data reflects it: a frontend engineer at a SaaS startup and a principal embedded engineer at a defense contractor both wear the title, and so do the ML platform engineer at a self-driving company and the back-office Java developer at a bank. The result is a fragmented stack, a polyglot hiring posture, and real money paid to candidates who specialize without losing the breadth.

To put numbers on it, we looked at every active Software Engineer posting on the InterviewStack.io job board as of May 2026: 48,207 listings, with skills extracted from descriptions and synonyms collapsed (so etl and data pipelines count once, gcp and google cloud count once). It is the largest single-role dataset we have analyzed.

Key findings

48,207 active Software Engineer postings analyzed across the live job board as of May 2026: the largest single-role dataset in our coverage.

No skill clears the 50% table-stakes line. Python leads at 34.8%, followed by Agile (29.8%), Java (27.3%), AWS (26.5%), and CI/CD (26.3%).

The polyglot reality is in the data: Java (27.3%), JavaScript (19.2%), TypeScript (17.7%), C++ (12.3%), and C# (12.0%) all appear in roughly one in eight to one in four postings.

Median US base salary is $140,000 (n=10,765), about $12K above the Data Engineer median and $53K above the Data Analyst median.

Specialization pays $19K to $30K above baseline for the top tier: Deep Learning, Computer Vision, Rust, dbt, Distributed Systems, Observability, gRPC, BigQuery, and Apache Spark all sit at $159K-$170K (n at least 75 each). A second tier of $9K to $17K premiums adds C++, Go, System Design, Next.js, Snowflake, Scala, GraphQL, and PyTorch.

Docker + Kubernetes is the strongest pair in the dataset at lift 3.75: about 70% of postings that mention Docker also ask for Kubernetes.

Only 4.0% of postings are entry-level (1,915 of 48,207); senior plus staff together are 44% of the market.

The US is 37.4% of postings, India 19.1%, and onsite remains the dominant work mode at 62.3% (hybrid 27%, remote 18%).

What Skill Families Define a Software Engineer Role in 2026?

Group every individual skill into the higher-level family it belongs to and count how many postings ask for at least one skill in that family. The picture is wide and shallow rather than narrow and deep.

Share of Software Engineer postings that ask for at least one skill in each family. A posting that mentions both Python and Java counts once under "Coding Languages".

The families that actually define the role:

Coding Languages: 72% of postings (Python, Java, JavaScript, TypeScript, C++, C#)
Tools & Infrastructure: 61% (automation, Git, Docker, Kubernetes, monitoring, Terraform)
Cloud Platforms: 34% (AWS, Azure, Google Cloud)
Querying & SQL: 33% (SQL itself, PostgreSQL, NoSQL, MySQL)
Process & Methodology: 31% (Agile, Scrum)
Data Engineering Foundations: 20% (Kafka, data pipelines)
Machine Learning & AI: 16% (machine learning, deep learning, generative AI)

Read the families against the Data Engineer post and the contrast is sharp. For Data Engineer, Data Engineering Foundations sits at 89% and Querying & SQL at 74%, because the role really is a stack. For Software Engineer, Coding Languages tops out at 72% and no other family clears two-thirds. The role does not converge on one thing.

The 34% cloud-platforms share is a useful pivot. Two-thirds of Software Engineer postings name no specific cloud at all, which fits the breadth of the title: client-side code, embedded systems, internal tooling, and on-premise enterprise work all still exist. But the postings that do name a cloud overwhelmingly cluster around AWS, with Azure and Google Cloud following.

What Are the Three Tiers of Individual Software Engineer Skills?

Drill into individual skills inside those families and three tiers emerge. The catch: the top tier is empty.

Top individual skills in Software Engineer postings, by share of listings that mention them. Skills above 50% would be table stakes; 20-50% are common; 5-20% are differentiators. No skill in this dataset clears the table-stakes threshold.

Table Stakes (50%+ of postings)

Empty. No individual skill appears in the majority of Software Engineer postings. The headline number is that Python, the most-demanded skill, shows up in just over a third of listings (34.8%). For comparison, Data Engineer postings have three skills above 70%, and Data Analyst postings have SQL above 80%. The Software Engineer market does not have a single hard filter.

What does that mean practically? It means there is no skill on a Software Engineer resume that screens you out of the majority of roles by being absent. It also means there is no skill that screens you in. Hiring filters happen lower in the stack: a Java backend posting filters for Java specifically, a React frontend role filters for React, an ML platform team filters for PyTorch. The role-level dataset shows the union, not the intersection.

Common Expectations (20-50% of postings)

This is where the role's center of gravity lives.

Python: 34.8% (Software Engineer + Python openings)
Agile: 29.8%
Java: 27.3% (Software Engineer + Java openings)
AWS: 26.5% (Software Engineer + AWS openings)
CI/CD: 26.3%
Automation: 24.0%
SQL: 21.9%
APIs: 21.1%

Two patterns jump out. First, Python has overtaken Java at the role level. Java has historically dominated enterprise SWE postings, but in this snapshot Python sits seven points ahead, pulled up by data tooling, ML platforms, internal scripting, and DevOps automation work that all sit under the "Software Engineer" umbrella. Java is still the second language of the role, not the first.

Second, Agile at 29.8% is the only soft-process skill in the common tier. That is high compared to most data roles and reflects how much SWE work happens in scrum-team org structures. Agile is a credentialing keyword in this market; calling it out on a resume is rarely wrong.

Differentiators (5-20% of postings)

This is where specialization shows up and where the salary curve starts to bend.

JavaScript: 19.2%
Kubernetes: 18.6%
TypeScript: 17.7%
Docker: 17.2%
Monitoring: 16.2%
Git: 16.1%
Azure: 16.1%
Debugging: 15.7%
React: 15.2%
Scalability: 14.5%
Microservices: 14.0%
Distributed Systems: 13.2% (Software Engineer + Distributed Systems openings)
Linux: 12.8%
C++: 12.3%
C#: 12.0%
Google Cloud: 11.8%
Observability: 11.3%
PostgreSQL: 10.9%
Testing: 10.8%
Nodejs: 8.7%
Kafka: 7.8%
System Design: 7.3%
Terraform: 6.9%
Machine Learning: 6.7%
LLMs: 5.0%

The differentiator tier is dense with cloud-native infrastructure skills (Kubernetes, Docker, Terraform, monitoring, observability) and with architecture concepts (microservices, distributed systems, system design, scalability). These are the skills that move a Software Engineer from "writes the feature" to "owns the system", and as the salary section shows next, the market pays for that distinction.

The frontend tier (React, TypeScript, JavaScript) sits in the same band, with TypeScript at 17.7% nearly matching JavaScript at 19.2%, evidence that the typed-frontend default has won. LLMs at 5.0% are the newest entrant on the differentiator list, sitting just above the 5% noise floor. They show up specifically in postings asking the engineer to build retrieval pipelines, evaluation harnesses, or LLM-backed product features rather than train foundation models.

Which Software Engineer Skills Pay More Than the Baseline?

Salary numbers below are restricted to US postings only (where wage-transparency laws produce consistent disclosure) so they're directly comparable. The numbers are base salary: equity, RSUs, bonuses, and sign-on are not disclosed in postings, so total compensation at top employers is meaningfully higher than what we report here, especially in tech and finance.

The overall median US base salary for Software Engineer postings is $140,000 (n=10,765). That sits about $12,000 above the Data Engineer median ($128,300) and about $53,000 above the Data Analyst median ($87,200). The SWE premium reflects a deeper coding bar and broader architectural responsibility, not a different geography mix.

Median US base salary in USD for postings that mention each skill, among US Software Engineer postings with structured salary data.

The biggest premiums attach to research, infrastructure, and modern-stack specialties. Skills with premiums of roughly $26K to $30K above the $140,000 baseline:

Deep Learning: $170,000 (n=182)
Computer Vision: $166,000 (n=414)

Skills with premiums of roughly $20K to $24K:

Rust: $164,400 (n=578)
dbt (a SQL transformation framework that runs inside the data warehouse): $163,800 (n=77)
Distributed Systems: $160,000 (n=2,017)
Observability: $160,000 (n=1,513)
gRPC (a high-performance remote-procedure-call framework used between backend services): $160,000 (n=291)
Datadog: $160,000 (n=233)
Incident Response: $160,000 (n=309)
Design Systems: $160,000 (n=244)
BigQuery: $160,000 (n=100)
Apache Spark: $159,200 (n=565)

Skills with premiums of roughly $13K to $17K:

PyTorch: $157,000 (n=237)
OpenAI: $155,300 (n=210)
Databricks: $155,000 (n=197)
Machine Learning: $153,100 (n=1,052)
GraphQL: $153,000 (n=399)
Scala: $152,800 (n=273)
LLM: $152,400 (n=577)

Smaller premiums of about $9K to $11K:

Llms: $151,000 (n=582)
Airflow (the open-source orchestrator most data teams use to schedule pipelines): $150,300 (n=253)
System Design: $150,000 (n=918)
Snowflake: $150,000 (n=251)
Next.js: $150,000 (n=252)
Go: $149,100 (n=422)
C++: $149,000 (n=1,841)
Data Pipelines: $149,000 (n=1,036)

Three patterns are worth naming. First, the AI/research stack pays the largest premiums. Deep Learning, Computer Vision, PyTorch, OpenAI, LLMs, and Machine Learning all sit $13K to $30K above baseline, which is the strongest signal in the dataset that companies are competing for engineers who can ship AI-adjacent product work. Second, the infrastructure stack still pays well: Distributed Systems, Observability, gRPC, and Apache Spark each sit at $160K, $20K above baseline. Third, the languages premium has shifted: Rust ($164.4K) and Go ($149.1K) are now the languages that move the needle, while traditional enterprise stacks (Java at $135K, C# at $124K) sit below the SWE baseline because they correlate with onshore-enterprise and global-services postings rather than the highest-paying tech segments.

The widely-asked skills sit close to baseline because they are the baseline. Python (n=4,867), TypeScript (n=2,568), AWS (n=3,115), Kubernetes (n=2,387), APIs (n=2,496), and React (n=1,724) all median exactly at $140,000. They are price-of-entry, not differentiators.

The practical takeaway: building the common-tier skills (Python or Java, AWS, CI/CD, SQL) keeps you in the running. Adding one differentiator that maps to a hiring concentration (Rust, distributed systems, observability tooling, or an AI specialty like PyTorch or LLM application work) is what moves your median offer by $20K or more. Our interview-prep courses cover the foundations across coding interviews, system design, and DSA; the question bank is where you drill the specific topics that come up in onsite rounds.

What Is the Dominant Software Engineer Skill Stack?

We computed every two-skill co-occurrence among the top 25 skills to find the combinations that show up together more often than chance.

The strongest pairs by lift, where lift greater than 1 means the two skills appear together more often than their individual frequencies would predict:

Skill pair	Postings that mention both	% of postings	Lift
Docker + Kubernetes	5,777	12.0%	3.75
AWS + Google Cloud	4,519	9.4%	2.99
AWS + Azure	5,277	10.9%	2.57
AWS + Kubernetes	5,387	11.2%	2.27
AWS + Microservices	4,009	8.3%	2.24
AWS + Docker	4,868	10.1%	2.21
CI/CD + Docker	4,618	9.6%	2.11
CI/CD + Kubernetes	4,556	9.5%	1.93
AWS + CI/CD	5,942	12.3%	1.76
Java + Kubernetes	4,065	8.4%	1.67

Each pair tells you something concrete about how postings actually compose skills:

Docker + Kubernetes (lift 3.75) is the strongest pair in the entire dataset, by a wide margin. About 7 in 10 postings that mention Docker also ask for Kubernetes, and most postings that mention Kubernetes ask for Docker too. The two are effectively treated as one skill at the hiring filter, the container-orchestration competence.
AWS + Google Cloud (lift 2.99) and AWS + Azure (lift 2.57) confirm that multi-cloud is a real hiring signal, not a buzzword. About 4 in 10 postings that name AWS also name Azure, and about 1 in 3 also name Google Cloud. Companies are asking for engineers who can move between providers, especially in enterprise and consulting work.
AWS + Kubernetes (lift 2.27), AWS + Microservices (lift 2.24), and AWS + Docker (lift 2.21) sketch the modern cloud-native backend cluster. If you are building a stack around AWS, the postings overwhelmingly assume you know the container and microservices layer that sits on top of it.
Python + SQL (lift 1.10, 4,034 postings) is barely above chance, a sharp contrast with Data Engineer postings where the pair has a lift of 1.15 and covers 58% of the market. For Software Engineers, the SQL-plus-Python base is just one of many bases.
Agile + Python (lift 0.89) is the dataset's most notable anti-correlation. Python-heavy postings are slightly less likely to mention Agile than chance would predict, evidence that Python-leaning postings concentrate in ML, research, and platform work where the Agile-keyword vocabulary is less common than in mainstream enterprise dev.

The big pattern: companies want a base layer (a language plus a cloud) and an operations layer (Kubernetes plus Docker plus CI/CD plus monitoring) and either a depth specialty (distributed systems, observability, AI/ML) or a stack specialty (frontend, mobile, embedded). The "language plus IDE" world of an earlier era does not exist in 2026 SWE hiring.

Who's Hiring at Which Seniority Level?

We tagged each posting's seniority based on title keywords (Senior, Lead, Principal, Junior, Intern). Postings with no explicit signal default to mid-level.

Seniority distribution of Software Engineer postings.

Mid-level: 52.1% (25,112 postings)
Senior: 30.1% (14,498) (senior Software Engineer openings)
Staff / Lead / Principal: 13.9% (6,682)
Entry: 4.0% (1,915)

Two things stand out. First, only 1 in 25 Software Engineer postings is explicitly entry-level. The market still hires juniors, but it does so under different titles (Junior Developer, Associate Engineer, Intern, internal training programs), so the "Software Engineer" label itself skews experienced. New-grad job seekers should expect to compete for a narrower share of postings than the dataset's headline suggests and should widen their search to Associate or Junior titles.

Second, the senior-and-above tiers (senior plus staff) make up 44% of postings, one of the deepest senior pipelines in tech. There is real career runway on the IC track, and the differentiator skills (distributed systems, system design, observability, multi-cloud) become required rather than nice-to-have once you cross into senior territory. Mid-to-senior is the largest single transition the dataset surfaces, and the salary curve bends there.

Where Are Software Engineer Jobs Located, and How Remote-Friendly Are They?

Geography for Software Engineer roles is more US-concentrated than for Data Engineer roles, where India and the US were nearly tied. The SWE market is still primarily a US market.

Top countries by share of Software Engineer postings.

United States: 37.4% (US-only Software Engineer openings)
India: 19.1%
Canada: 4.2%
United Kingdom: 3.4%
Germany: 3.0%
Poland: 1.5%
Australia: 1.4%
Singapore: 1.2%

The US has roughly twice the share of any other country, which is unusual: most other tech roles we have analyzed cluster the US and India closer together. The likely explanation is that the Software Engineer title in India is split across more local-language variants (Software Developer, Software Engineer L1/L2, Application Developer), so the resolved-role filter picks up fewer of them. Even so, India remains the second-largest single market by a wide margin.

The remote-first assumption holds less for Software Engineers than the headlines suggest.

Share of Software Engineer postings tagged with each work mode. Some postings carry multiple tags (e.g., "Hybrid or Remote"), so percentages sum to more than 100%.

Onsite: 62.3% of postings (30,030)
Hybrid: 26.6% (12,826)
Remote: 17.5% (8,453) (fully-remote Software Engineer openings)

Postings can carry multiple work-mode tags when a company says "Hybrid or Remote", which is why the percentages sum to more than 100%. The takeaway: nearly 2 in 3 Software Engineer postings still default to onsite, lower than other tech roles we have analyzed. The dataset is heavy with defense, aerospace, chip-design, and financial-services employers (visible in the top-companies list below), and those segments have remained onsite-first even as product SaaS has gone remote. The fully-remote share is concentrated in product tech and SaaS; if remote work is a hard requirement for your search, expect to filter down to roughly the same 17.5% of the market.

Who's Hiring Software Engineers in 2026?

The top hiring companies on our board span global consulting, GPU and chip design, defense and aerospace, banking, and product SaaS, a more diverse mix than any other tech role we have surveyed.

Top companies by active Software Engineer postings. Counts include all locations of the same job.

Accenture: 3,126 postings (global consulting)
Speechify: 822 (consumer AI / accessibility)
NVIDIA Corporation: 438 (GPU and AI infrastructure)
Anduril Industries: 269 (defense technology)
Boardroom Appointments: 248 (staffing)
Cisco: 243 (networking and security)
AgileEngine: 243 (software services)
Softtest Pays: 220 (QA and services)
Northrop Grumman: 216 (defense and aerospace)
Cadence Design Systems: 213 (chip-design EDA)
PradeepIT: 211 (staffing)
Leidos: 206 (defense and government IT)
RELX Group: 188 (information services)
Mastercard: 174 (payments)
SpaceX: 166 (aerospace)
Autodesk: 155 (engineering software)
Barclays: 155 (banking)
Exadel: 151 (software services)

A few specific takeaways. Accenture's lead is enormous: at 3,126 postings, the consulting firm is hiring nearly 4 times as many Software Engineers as the next-largest employer. Defense and aerospace is unusually well-represented for a SWE list (Anduril, Northrop Grumman, Leidos, SpaceX), reflecting the post-2024 buildup in defense-tech and space launch. Chip-design and GPU work shows up next (NVIDIA, Cadence), driven directly by the AI hardware build-out. The mainstream-tech companies you might expect to see at the top are present but smaller in posting volume than the consulting and defense players.

For specific company processes, our interview preparation guides break down the rounds, topic priorities, and behavioral expectations company by company.

How to Use This in Your Job Search

If you are preparing for a Software Engineer job hunt, the data points to a clear sequence.

1. Pick your stack, not just your skills. Because no skill is table stakes at the role level, the filter you actually face is at the stack level. A Java backend hire wants Java specifically. An ML platform team wants Python plus PyTorch plus distributed systems. A frontend role wants React plus TypeScript plus a design-system fluency. Pick the stack that matches the companies you actually want to work for, then optimize for that stack rather than for the union of every skill in the dataset.

2. Lock down the cloud-native cluster. Docker + Kubernetes is the strongest pair in the data, and AWS pairs strongly with both. If you are targeting backend or platform roles, the cloud-native cluster (Docker, Kubernetes, AWS or another cloud, CI/CD, observability) is what hiring managers assume you can speak to, even if no single skill is named in every posting.

3. Add one differentiator that moves the salary curve. The salary data is unambiguous: the skills that pay $20K or more above baseline are the differentiators, not the table stakes. Distributed Systems, Rust, gRPC, Apache Spark, observability tooling, and AI/ML specialties (PyTorch, LLMs, Machine Learning) all move your median US base salary by $13K to $30K. Pick one that fits the kind of system you want to build and learn it deeply enough to talk through trade-offs in an onsite.

4. Drill the topics, then practice the rounds. Reading about Software Engineer skills is easy; performing under interview conditions is the hard part. Our interview-prep courses cover the foundations across DSA, system design, and coding interviews. The question bank lets you drill specific topics (system design, distributed systems, concurrency, algorithms) one at a time. AI mock interviews let you practice the full round under realistic conditions, with on-demand feedback on coding and system-design questions specifically.

5. Filter the job board for your stack. Browse current Software Engineer openings on the InterviewStack.io job board and combine role and skill filters to narrow to your exact stack. For example, Software Engineer + Rust for systems work, or Software Engineer + Kubernetes + AWS for cloud-native backend. The board updates daily, so the listings are current.

FAQ

Q. What skills do companies want for Software Engineer roles in 2026?

No single skill is table stakes. The role is unusually fragmented across stacks. Python leads at 34.8% of postings, followed by Agile (29.8%), Java (27.3%), AWS (26.5%), CI/CD (26.3%), Automation (24.0%), SQL (21.9%), and APIs (21.1%). Below that, JavaScript, Kubernetes, TypeScript, and Docker each appear in 17-19% of postings. The pattern is polyglot: companies want fluency across languages, cloud, and DevOps rather than mastery of one stack.

Q. What is the median Software Engineer salary in 2026?

The median US base salary across 10,765 Software Engineer postings with disclosed US salary is $140,000. That figure excludes equity, bonuses, RSUs, and sign-on, so total compensation at top employers (especially in tech and finance) runs meaningfully higher than the base.

Q. Which Software Engineer skills pay the highest premium over the role baseline?

Among US postings, the largest premiums attach to research, infrastructure, and modern-stack specialties. Deep Learning ($170K, n=182) and Computer Vision ($166K, n=414) each sit $26K to $30K above the $140,000 baseline. Rust ($164.4K, n=578) and dbt ($163.8K, n=77) clear roughly $24K. Distributed Systems ($160K, n=2,017), Observability ($160K, n=1,513), gRPC ($160K, n=291), and BigQuery ($160K, n=100) each pay about $20K above baseline. PyTorch, GraphQL, Scala, LLMs, System Design, Snowflake, Next.js, Go, and C++ follow with premiums in the $9K to $17K range.

Q. Is Software Engineer a good entry-level role to break into?

Only 4.0% of Software Engineer postings are explicitly entry-level (1,915 of 48,207). That is higher than the 3% entry-level share in Data Engineer hiring, but still narrow. Mid-level (52.1%) and senior-or-above (44%) roles dominate, so most paths in start at junior internal-tools, support-engineering, or backend roles before competing for SWE-titled openings.

Q. Where are most Software Engineer jobs located, and how remote-friendly are they?

The United States is the largest market by a wide margin at 37.4% of postings (18,007), followed by India at 19.1% (9,210). Canada (4.2%), the United Kingdom (3.4%), Germany (3.0%), Poland (1.5%), Australia (1.4%), and Singapore (1.2%) round out the top countries. Onsite is the dominant work mode at 62.3% of postings; hybrid sits at 26.6% and fully-remote at 17.5%.

Q. Which companies hire the most Software Engineers in 2026?

Accenture leads by a wide margin with 3,126 active postings, followed by Speechify (822), NVIDIA (438), Anduril Industries (269), Boardroom Appointments (248), Cisco (243, with another 200 under a separate brand entry), AgileEngine (243), Softtest Pays (220), Northrop Grumman (216), Cadence Design Systems (213), PradeepIT (211), Leidos (206), RELX Group (188), Mastercard (174), and SpaceX (166). The mix spans global consulting, GPU and chip design, defense and aerospace, banking, and product SaaS.

Q. What is the dominant Software Engineer skill stack in 2026?

There is no single canonical stack. The strongest co-occurrence in the dataset is Docker + Kubernetes (lift 3.75, 12.0% of postings ask for both). AWS pairs strongly with Google Cloud (lift 2.99) and Azure (lift 2.57), evidence that multi-cloud fluency is genuinely valued. AWS combines with Kubernetes (lift 2.27), Microservices (lift 2.24), Docker (lift 2.21), CI/CD (lift 1.76), and Java (lift 1.56) to form the cloud-native cluster that defines modern backend roles.

Final Thoughts

The Software Engineer title in 2026 is the broadest one in tech hiring, and the data reflects that breadth: no skill clears the table-stakes line, the role spans frontend through embedded through ML platforms, and the salary curve rewards specialization rather than coverage. The most important move for a candidate is to stop optimizing for "Software Engineer" as one role and start optimizing for the specific stack you want to build a career on, with one differentiator (Rust, distributed systems, observability, or an AI/ML specialty) that bends the salary curve in your favor.

We will refresh this analysis quarterly so the trend lines stay current.

Backend Developer vs DevOps Engineer 2026: Salary, Skills

Gnana — Tue, 19 May 2026 03:23:52 +0000

Which Role Should You Pick in 2026?

Backend Developer is the role that builds the product; DevOps Engineer is the role that builds the platform the product runs on. Among US postings, Backend pays a $150,000 median base salary versus $131,500 for DevOps (an $18,500, 14.1% gap), but the two markets are nearly the same size at 7,257 and 6,908 active postings on the InterviewStack.io job board in May 2026. The skill sets share about 43% of their top-30 entries (cloud, containers, CI/CD, Python, monitoring), so the choice is less about learning a new universe and more about which side of the wire you want to live on: writing the application logic, or operating the infrastructure underneath it.

	Backend Developer	DevOps Engineer
Median US base salary	$150,000 (n=545)	$131,500 (n=1,103)
Active postings	7,257	6,908
Top skill	AWS (43%)	CI/CD (63%)
Entry-level share	2.0%	2.0%
Remote share	30%	23%
Skill overlap (Jaccard)	43%	43%

Key Findings

Median US base salary is $150,000 for Backend Developer (n=545) versus $131,500 for DevOps Engineer (n=1,103), an $18,500 (14.1%) premium for Backend.

Backend Developer has 7,257 active postings versus 6,908 for DevOps Engineer, a 1.05x volume ratio that makes these two of the most evenly-matched career markets in tech.

The two roles share about 43% of their top-30 skill sets, dominated by AWS, Kubernetes, Docker, CI/CD, Python, monitoring, and observability.

Entry-level access is equally narrow: 2.0% of postings on each side are explicitly entry-level, one of the tightest doors in tech.

Backend is more remote-friendly (30% vs 23%) and more globally distributed; DevOps is 29% US-anchored and leans more hybrid (33% vs 23%).

Pulumi ($170,000) leads DevOps premiums; Rust ($179,500) leads Backend, with LLM-related skills paying $20-25K above baseline on both sides.

What Does Each Role Actually Do?

Backend Developer is an application-engineering role. The week is writing API services in Java, Python, or TypeScript that talk to PostgreSQL, Redis, and Kafka, designing the REST or gRPC contracts other teams consume, modeling data so that the database does not become the bottleneck, and shipping features through CI/CD into containers that run on someone else's Kubernetes cluster. The exclusive-skill list (Microservices 31%, Distributed Systems 26%, PostgreSQL 25%, Kafka 19%, Node.js 18%, TypeScript 16%, Spring 14%) is the modern web-services stack. The output is product functionality that an end user or another team's service actually consumes.

DevOps Engineer is a platform-engineering role. The work is provisioning and maintaining the cloud accounts, Kubernetes clusters, CI/CD pipelines, observability stacks, and secrets management that every application team depends on. The exclusive list reads like an operations playbook: Infrastructure as Code at 35%, Linux at 30%, Bash at 27%, Jenkins at 25%, Ansible at 23%, GitLab at 20%, Prometheus at 20%, Grafana at 19%. The output is a paved road: a Terraform module that spins up a service, a GitOps pipeline that deploys it, a Prometheus dashboard that watches it, and an on-call rotation that keeps it healthy at 3 a.m. Think of Backend as the team writing the code that ships; DevOps as the team building the conveyor belt and runway it ships on.

What Skills Do Both Roles Require?

Both roles share the modern cloud and operations core: AWS, Kubernetes, Docker, CI/CD, Python, monitoring, and observability all appear in both top-30 lists. The Jaccard overlap of 43% is the second-clearest signal in this comparison, and the practical translation is that an engineer who has spent two years on either side already has a head start on the other.

Share of postings that ask for each skill, comparing Backend Developer (n=7,257) to DevOps Engineer (n=6,908). Skills shown are drawn from the union of each role's top set.

The weights are not symmetric. The top five shared skills (CI/CD, AWS, Kubernetes, Python, monitoring) sit in roughly half of DevOps postings or more (46-63%) while Backend tops out at AWS's 43% across the same set. The gap widens on platform-defining tooling: CI/CD splits 63% versus 38%, automation 55% versus 15%, Terraform 46% versus 13%. Each gap tells the same story: DevOps owns the platform, Backend ships against it. On Docker (37% vs 34%) and observability (28% vs 22%) the two come closer to parity, but the framing still differs: DevOps builds the cluster and the dashboards; Backend writes code that runs inside them and emits metrics into them.

Where Do the Roles Diverge?

Exclusive to Backend Developer

The Backend side of the fork is the web-services and data-layer stack that does not show up on a DevOps job description.

Microservices: 31%
Distributed Systems: 26% (Backend + Distributed Systems openings)
PostgreSQL: 25%
Kafka: 19% (Backend + Kafka openings)
Node.js: 18%
TypeScript: 16%
NoSQL: 15%
JavaScript: 15%
Spring: 14%
MySQL: 13%

Backend owns the service boundary and the data underneath it. A posting that asks for Microservices (31%), Distributed Systems (26%), PostgreSQL (25%), and Kafka (19%) expects the candidate to reason about consistency, latency, partial failure, and event flow as daily design work. The day is spent inside a service codebase in Java, Node.js, TypeScript, Spring, or Python, not inside a Terraform module.

Exclusive to DevOps Engineer

The DevOps side is the infrastructure-as-code, CI, configuration-management, and observability stack that defines platform engineering.

Infrastructure as Code: 35% (DevOps + IaC openings)
Linux: 30%
Bash: 27%
Jenkins: 25%
Ansible: 23%
GitLab: 20%
Prometheus: 20%
Grafana: 19%
GitHub Actions: 18%
Azure DevOps: 13%

DevOps roles assume the platform is defined in repeatable text, not clicked through a console (IaC at 35%), and the day-to-day tooling lives at the shell (Linux 30%, Bash 27%). The presence of Jenkins, GitLab, GitHub Actions, and Azure DevOps in the same exclusive list signals that DevOps engineers are expected to be portable across CI vendors. Prometheus and Grafana at 20% and 19% mean a real share of postings want a candidate who can stand up an observability stack from scratch, not just read existing dashboards.

Which Pays More?

Among US postings, Backend Developer leads at a $150,000 median base salary (n=545) versus $131,500 for DevOps Engineer (n=1,103), an $18,500 (14.1%) gap. Salary numbers below are US-only base salary. Equity, RSUs, bonus, and sign-on are not disclosed in postings and are not in this dataset, so total compensation at top employers runs meaningfully higher than these figures, particularly at large product companies for Backend and at hyperscalers and finance for DevOps.

Median US base salary in USD for postings that mention each skill, restricted to US postings with structured salary data.

The 14% headline gap is best read as a product-versus-platform premium. The companies bidding hardest for Backend talent are product companies whose revenue depends on shipping application features; the companies bidding for DevOps talent include those same employers plus a long tail of services firms and large enterprises where the platform team is a cost center. The wider DevOps sample (n=1,103 versus 545) pulls the median toward the enterprise middle of the market, while the Backend sample is more concentrated in product-first employers paying at the high end.

At the skill level, the premium pattern flips for specialists. The biggest Backend premiums attach to systems and AI specialties: Rust at $179,500 (n=38, about $29.5K above baseline), Data Modeling and Generative AI both at $175,000 (about $25K above), LLMs, OpenAI, and Machine Learning all at $170,000 (about $20K above), and Observability at $168,900 (n=149, about $18.9K above). C++ at $168,000 and Terraform at $167,000 round out the top tier, both signals that Backend roles paying at the top of the band expect engineers who can drop into systems work or own their own infrastructure.

For DevOps, the biggest premiums attach to modern infrastructure tooling and SaaS observability. Pulumi leads at $170,000 (n=39, about $38.5K above the $131,500 baseline), the steepest single-skill premium in the comparison, reflecting how new the multi-language IaC category still is. OpenTelemetry and PagerDuty both clear $160,000 (about $28.5K above), and Datadog at $159,400 (n=88) sits just below. The AWS-serverless cluster (Lambda $155K, ECS $154,100) and the AI cluster (LLM $152,100, Machine Learning $151,200, LLMs $150,900, RAG $150,100) all land near $150-155K. Notice that GitOps ($150,000), Distributed Systems ($150,000), and IAM ($150,000) match the Backend baseline exactly: a DevOps engineer with those specialties earns at the Backend median rate.

Which Has More Job Openings?

The volume question has an unusually flat answer. Backend Developer's 7,257 active postings edge out DevOps Engineer's 6,908 by just 349 listings, a 1.05x ratio that makes these two of the most evenly-matched roles in tech. The structural reason is symmetric demand: every company that runs a software product needs Backend engineers to build it and DevOps engineers to operate the platform it runs on. Where many comparisons feature a clearly larger pool, here the choice does not penalize you on volume either way.

The accessibility picture is identically narrow on both sides. Entry-level postings make up 2.0% of Backend Developer listings (144 of 7,257) and 2.0% of DevOps Engineer listings (139 of 6,908), one of the tightest entry doors in tech. Companies expect production experience for both: shipping a service for Backend, owning infrastructure for DevOps. Career switchers typically route through associate-engineer, support-engineer, or platform-intern roles before stepping into either title. The senior tiers tell different stories: Backend splits almost evenly between mid-level and senior individual contributors (43% each, plus 12% staff), while DevOps is sharply mid-heavy (56% mid-level, 30% senior, 12% staff), which says DevOps teams tend to scale by adding mid-level operators while Backend teams scale by adding senior engineers who can own a service end-to-end.

Geography reflects the buying patterns. DevOps is 29% US with India (13%), the UK (5%), Germany (4%), Canada (3%), Australia (3%), and Poland (3%) trailing, mirroring where large enterprise IT spend concentrates. Backend is only 17% US, with a wider European and Latin American long tail (Germany 4%, Poland 3%, Brazil 3%, Canada 3%, UK 3%, Spain 3%, Portugal 3%). Work-mode reads the same way: 30% of Backend postings are remote versus 23% for DevOps, and DevOps leans more hybrid (33% vs 23%) because platform teams need office access for incident response and on-call. The top hiring lists look more alike than different at the very top. Backend's leaders are staffing and services firms placing engineers at product companies (AgileEngine at 519 postings, PradeepIT at 103, Nexthire at 84), with product employers like Coupang (65), Tether (46), and Encora further down the list. DevOps is also led by staffing firms (Softtest Pays Pty Ltd at 101 postings and AgileEngine at 91 take the top two slots, with recruiter Boardroom Appointments at 52 in fifth), but large enterprises and consultancies show up far higher on the DevOps list than on the Backend one: Accenture (76), Booz Allen Hamilton (62), ING (50), PricewaterhouseCoopers (49), Barclays (49), and Thales Group (42) all rank in the top nine. The signal is not that DevOps hiring is enterprise-dominated, but that platform work is centralized inside Fortune 500 IT and big-four consulting practices more visibly than Backend work is.

Which Should You Choose?

Choose Backend Developer if you:

Want to build the product itself: design APIs, model data, and ship application features that an end user or another team consumes.
Prefer working inside a service codebase in Java, Python, TypeScript, or Node.js, with PostgreSQL or Kafka as your data layer and microservices as your architecture default.
Care about the higher median pay (14% premium) and the more remote-friendly mix (30% vs 23%), and you want a career ladder that rewards depth in distributed systems, data modeling, and increasingly AI-integration specialties like LLMs and generative AI.

Choose DevOps Engineer if you:

Want to build and run the platform every other team ships on: Terraform modules, Kubernetes clusters, CI/CD pipelines, observability stacks, and the on-call rotation that keeps them healthy.
Are comfortable at the shell, in YAML, and inside Linux, and you enjoy automation work that pays off across dozens of downstream teams rather than a single product surface.
Care about a US-anchored market (29% of postings) with strong demand at large enterprises and consultancies, and you want a path where modern IaC, observability SaaS, and AWS-serverless specializations (Pulumi, Datadog, Lambda, ECS) can push your offer $20-38K above the role baseline.

If the choice is close, the data points to a useful tiebreaker: the highest-paying specialties on both sides converge around observability, distributed systems, and AI-integration work. An engineer who picks either role and invests in those areas closes most of the median-pay gap. Our interactive courses cover the foundations across systems, distributed systems, and cloud; the question bank lets you drill API design, scalability, CI/CD design, and infrastructure topics one at a time; and AI mock interviews put you under realistic onsite conditions for either track.

FAQ

Q. What's the salary difference between Backend Developer and DevOps Engineer in 2026?

The median US base salary is $150,000 for Backend Developer (n=545) versus $131,500 for DevOps Engineer (n=1,103), an $18,500 (14.1%) premium for the Backend role. Both figures are base only and exclude equity, RSUs, and bonuses, so total compensation at top employers runs meaningfully higher than these numbers, especially at large product companies for Backend and at hyperscalers and finance for DevOps.

Q. How much do Backend Developer and DevOps Engineer skills overlap?

About 43% (Jaccard similarity on each role's top-30 skills), a moderate overlap that reflects how much cloud and operations work has bled into application engineering. Both roles share AWS, Kubernetes, Docker, CI/CD, Python, monitoring, and observability. The weights diverge most sharply on the top of the stack: DevOps demands these shared skills in 28-63% of postings (CI/CD at 63% leads, then AWS, Kubernetes, and Python near 51-52%) while Backend asks for them in 22-43% (AWS at 43% leads). On Docker and observability the two roles are closer to parity. Beyond the cloud core, Backend pulls toward Java, APIs, microservices, PostgreSQL, and Kafka; DevOps pulls toward Terraform, Linux, Bash, Jenkins, and Ansible.

Q. Which role has more job openings?

Backend Developer is the slightly larger market: 7,257 active postings versus 6,908 for DevOps Engineer on the InterviewStack.io job board in May 2026, a 1.05x volume ratio and a difference of just 349 listings. These are two of the most heavily-hired roles in tech, and they sit within a few percent of each other on the demand side.

Q. Which role is easier to enter at the junior level?

Neither role is easy to break into. Both sit at almost exactly 2% entry-level share: 144 of 7,257 Backend Developer postings (2.0%) and 139 of 6,908 DevOps Engineer postings (2.0%). Companies overwhelmingly expect production experience for both. Career switchers typically route through associate-engineer, support-engineer, or platform-intern roles before stepping into either title.

Q. Should I become a Backend Developer or a DevOps Engineer in 2026?

Pick Backend Developer if you want to build the product itself: write Java or Python services, design APIs, model relational and NoSQL data, and ship features that users see, with the higher median pay and the larger code-craft career ladder. Pick DevOps Engineer if you want to build and run the platform that everyone else ships on: Terraform-managed infrastructure, Kubernetes clusters, CI/CD pipelines, observability stacks, and a US-anchored market where 56% of postings are mid-level and most large enterprises have a dedicated platform team.

Q. Which specific skills give the biggest salary premium in each role?

For Backend Developer, the highest-paying skills are systems and AI specialties: Rust ($179,500, +$29.5K above the $150K baseline), Data Modeling ($175,000, +$25K), Generative AI ($175,000, +$25K), LLMs ($170,000, +$20K), and Observability ($168,900, +$18.9K). For DevOps Engineer, the biggest premiums attach to modern infrastructure and observability tooling: Pulumi ($170,000, +$38.5K above the $131,500 baseline), OpenTelemetry ($160,000, +$28.5K), PagerDuty ($160,000, +$28.5K), Datadog ($159,400, +$27.9K), and serverless AWS skills like Lambda ($155,000, +$23.5K) and ECS ($154,100, +$22.6K).

Q. Where are the jobs and how remote-friendly is each role?

DevOps Engineer is markedly more US-concentrated: 29% of postings are in the US versus 17% for Backend Developer. Backend has a slightly smaller Indian presence (12% versus 13%) and a more globally distributed long tail across Germany, Poland, Brazil, Canada, and the UK. Backend is the more remote-friendly of the two: 30% of postings are tagged remote versus 23% for DevOps; onsite share is nearly identical at 52% and 53%, but DevOps leans more hybrid (33% versus 23%) because platform teams often need office access for incident response and on-call rotations.

Final Thoughts

Backend Developer and DevOps Engineer share a cloud-operations core, not a job. Backend writes the product: APIs, microservices, distributed systems, and data layers in Java, Python, and TypeScript. DevOps builds and runs the platform: Terraform-managed infrastructure, Kubernetes, CI/CD pipelines, and observability stacks. Backend pays 14% more at the median; DevOps is a slightly smaller but nearly equal market, more US-concentrated, and more hybrid by default. Both have unusually narrow entry doors at 2% entry-level share, and both reward modern observability, distributed-systems, and AI-integration specialties at the top end. Browse live Backend Developer postings or DevOps Engineer postings on the InterviewStack.io job board.

Backend Developer vs Embedded Developer 2026: Salary, Skills

Gnana — Sat, 16 May 2026 23:32:11 +0000

The Short Answer

Backend Developer is the larger, higher-paying, cloud-native half of the systems-software market. Embedded Developer is the smaller, hardware-anchored half with a much wider door at entry level. Among US postings, Backend Developer pays a $150,000 median base salary versus $134,600 for Embedded Developer (a $15,400, 11.4% gap), and Backend postings outnumber Embedded by 7,234 to 2,589 on the InterviewStack.io job board in May 2026. The two skill sets share only about 20% of their top-30 skills, so the choice is really a choice between two different software-engineering disciplines that happen to share a name suffix.

	Backend Developer	Embedded Developer
Median US base salary	$150,000 (n=547)	$134,600 (n=848)
Active postings	7,234	2,589
Top skill	AWS (43%)	Python (37%)
Entry-level share	2.0%	6.7%
Remote share	30%	8%
Skill overlap (Jaccard)	20%	20%

Key Findings

Median US base salary is $150,000 for Backend Developer (n=547) versus $134,600 for Embedded Developer (n=848), a $15,400 (11.4%) gap.

Backend Developer has 7,234 active postings versus 2,589 for Embedded Developer, a 2.79x volume ratio.

The two roles share only 20% of their top-30 skill sets, one of the lowest overlaps between any two software-engineering titles we have compared.

Embedded Developer is over three times more accessible at entry level: 6.7% of postings versus 2.0% for Backend Developer.

Embedded is heavily onsite (77%) and US-anchored (53% of postings); Backend is 30% remote and only 17% US.

Computer Vision ($166,000) is the highest-paying Embedded skill; Rust ($179,500) leads Backend, with LLM-related skills paying $20-25K above baseline.

What Does Each Role Actually Do?

Backend Developer is a cloud-services role. The week is writing API services in Java, Python, or TypeScript that run inside containers on AWS, Azure, or Google Cloud, designing and maintaining microservices, owning relational and NoSQL databases (PostgreSQL, MySQL, Redis), building CI/CD pipelines, and operating the result with monitoring, observability, and alerting. The exclusive-skill list (AWS at 43%, APIs at 35%, Docker at 34%, Kubernetes at 34%, Java at 33%, Microservices at 31%) reads exactly like the modern web-service stack. The output is usually a service that handles millions of user requests reliably.

Embedded Developer is a hardware-adjacent role. The work is writing C++ (and increasingly Rust) that runs on a microcontroller, board, or system-on-chip, debugging with logic analyzers, JTAG probes, and oscilloscopes, building Linux images for the device, and prototyping firmware against breadboards and reference hardware. The exclusive list is short and concrete: C++ at 29%, Linux at 23%, Prototyping at 9%. The output is code that runs on physical devices: chips, satellites, automotive ECUs, drones, medical instruments, and consumer products. Think of Backend as the engineer for cloud-scale software; Embedded as the engineer for the firmware that makes a physical product work.

What Skills Do Both Roles Require?

The shared core is small and concentrated. Python is the only skill that exceeds 25% in both roles (29% Backend, 37% Embedded), and Embedded actually leans on it harder than Backend does, primarily for build automation, test scripts, and ML inference on the edge. Beyond Python, the overlap is mostly engineering hygiene: Git, automation, and debugging.

Share of postings that ask for each skill, comparing Backend Developer (n=7,234) to Embedded Developer (n=2,589). Skills shown are drawn from the union of each role's top set.

Even within the "shared" skills, the weights diverge. Debugging appears in 33% of Embedded postings versus 12% of Backend ones, because Embedded engineers debug at the silicon-and-signal level where a unit test cannot reach. CI/CD flips the same way: 38% of Backend postings versus 7% of Embedded, since Backend ships changes daily through automated pipelines while Embedded ships firmware on a release cadence measured in months. Monitoring (25% versus 6%) and Agile (24% versus 10%) tell you the same story from the operational side: Backend lives inside a continuously-deployed product cycle; Embedded lives inside a hardware product cycle.

Where Do the Roles Diverge?

Exclusive to Backend Developer

The Backend side of the fork is dominated by the cloud-native web-services stack.

AWS: 43% (Backend + AWS openings)
APIs: 35%
Docker: 34%
Kubernetes: 34% (Backend + Kubernetes openings)
Java: 33%
Microservices: 31%
Scalability: 26%
Distributed Systems: 26%
PostgreSQL: 25%
SQL: 25%

This is a single, coherent picture. A Backend posting is, by default, a containerized Java or Python service that talks to a relational database, sits behind an API gateway, and runs as one of many microservices on a Kubernetes cluster in AWS. The combination of Distributed Systems (26%), Scalability (26%), Microservices (31%), and Kubernetes (34%) means a real share of postings expect candidates to think about consistency, latency, and partial failure as part of normal day-to-day design work.

Exclusive to Embedded Developer

The Embedded side has fewer high-frequency exclusives, which itself is a signal: the role has a deep, narrow specialty rather than a broad surface area.

C++: 29% (Embedded + C++ openings)
Linux: 23%
Prototyping: 9%

C++ is the language that defines the role. It appears in nearly three of every ten Embedded postings, while Backend uses C++ only as a high-paying differentiator outside its top-30. Linux at 23% reflects how much modern embedded work runs on Yocto, Buildroot, or a real-time Linux distribution rather than bare-metal microcontrollers. Prototyping at 9% sounds low, but it is a unique signal: it tells you a meaningful slice of Embedded jobs involves building physical proof-of-concept hardware, not only writing code against existing devices.

Which Pays More?

Among US postings, Backend Developer leads at a $150,000 median base salary (n=547) versus $134,600 for Embedded Developer (n=848), a $15,400 (11.4%) gap. Salary numbers below are US-only base salary. Equity, RSUs, bonus, and sign-on are not disclosed in postings and are not in this dataset, so total compensation at top employers runs meaningfully higher than these figures, particularly at large tech firms for Backend and at semiconductor and defense employers for Embedded.

Median US base salary in USD for postings that mention each skill, restricted to US postings with structured salary data.

The headline gap is best read as a market-supply premium. Backend skills transfer across nearly every company that runs a website, so the demand pool is enormous and recruiters bid the median up. Embedded skills concentrate in a smaller set of hardware-centric employers, where supply and demand sit closer to balance.

The skill-level salary picture flips that logic for specialists. The biggest Backend premiums attach to systems and AI specialties: Rust at $179,500 (n=38, about $29.5K above baseline), Generative AI and OpenAI both at $175,000 (about $25K above), LLMs at $170,000 (n=36, about $20K above), and Observability at $168,900 (n=149, about $18.9K above). Distributed Systems at $165,000 (n=171, about $15K above) and Terraform at $164,000 (n=65) round out the top tier.

For Embedded, the biggest premiums sit on the boundary with adjacent software domains. Computer Vision leads at $166,000 (n=85, about $31.4K above the $134,600 baseline), reflecting the autonomy and robotics employers near the top of the hiring list. Rust sits at $154,500 (n=28, about $19.9K above), Machine Learning at $153,500 (n=46, about $18.9K above), and the in-domain trio of C++, CI/CD, and Scalability all reach $150,000 (about $15.4K above baseline). C++ pays $163,600 in Backend but $150,000 in Embedded, an instructive contrast: in Backend, knowing C++ is rare and rewarded; in Embedded, C++ is the price of admission.

Which Has More Job Openings?

Backend Developer is by far the larger market. The 7,234 active Backend postings outnumber Embedded's 2,589 by roughly 2.79x, a 4,645-listing gap. The structural reason is straightforward: every company that runs a website, an API, or an internal tool needs Backend engineers. Embedded demand concentrates in a smaller set of industries (semiconductors, defense, aerospace, automotive, medical devices, consumer hardware), so the volume ceiling is lower even when those industries are healthy.

The accessibility picture inverts. Embedded Developer is materially easier to enter: 6.7% of postings are explicitly entry-level (173 of 2,589) versus 2.0% for Backend Developer (145 of 7,234), more than three times the proportional share. The senior-plus-staff tier in Backend (43% senior, 12% staff) is heavier than in Embedded (27% senior, 17% staff), which means the Backend ladder leans toward people who are already in the field; Embedded teams are more willing to hire new graduates with the right CS or electrical-engineering foundation and train them on the toolchain.

Geography splits sharply. Embedded is a 53%-US market, with India (8%), Germany (4%), the UK (3%), and Canada (3%) trailing. The US concentration reflects where the customers are: defense contractors, aerospace primes, semiconductor companies, and autonomy-focused startups all anchor in the US. Backend is the opposite: only 17% of postings are US-based, with a substantial Indian presence (12%) and a long European tail (Germany 4%, UK 3%, Poland 3%, Spain 3%, Portugal 3%, Brazil 3%). Work-mode follows the same logic: 30% of Backend postings are remote and 51% onsite, while Embedded is 77% onsite with only 8% remote, because firmware engineers need physical access to the hardware they target. The top hiring lists confirm the split: Backend's leaders are consulting and software-services firms (AgileEngine, PradeepIT, Nexthire) plus product companies like Coupang, GitLab, and Tether; Embedded is dominated by semiconductors, defense, and aerospace (NVIDIA, Anduril, SpaceX, Marvell, Cisco, Cesium Astro, Analog Devices, Northrop Grumman, Honeywell).

Which Should You Choose?

Choose Backend Developer if you:

Want to build cloud services and APIs that scale to millions of users: Java, Python, AWS, microservices, and Kubernetes form the core stack.
Prefer a fast feedback loop where code ships through CI/CD on a daily cadence and you operate the running service yourself.
Care about the bigger market and the slightly higher median pay, plus the much wider remote-work mix (30% versus 8%).

Choose Embedded Developer if you:

Want to write C++ that runs on physical devices: chips, satellites, cars, drones, medical instruments, consumer hardware.
Have or want a comfort zone near hardware: oscilloscopes, JTAG, board-bringup, and Linux build systems like Yocto.
Care about the materially wider entry door (6.7% versus 2.0% entry-level), are open to onsite work in US hardware hubs, and want to work for companies like NVIDIA, Anduril, SpaceX, or Marvell.

If the choice is still close, the salary skill data is your tiebreaker. The highest-paying Embedded skills (Computer Vision, Rust, Machine Learning) are the same skills that command premiums in Backend, so a candidate who picks Embedded and invests in those specializations closes most of the median-pay gap. Our interactive courses cover the foundations across systems, distributed systems, and C++; the question bank lets you drill API design, scalability, and embedded-systems topics one at a time; and AI mock interviews put you under realistic conditions for either track.

FAQ

Q. What's the salary difference between Backend Developer and Embedded Developer in 2026?

The median US base salary is $150,000 for Backend Developer (n=547) versus $134,600 for Embedded Developer (n=848), a $15,400 (11.4%) premium for the Backend role. Both figures are base only and exclude equity, RSUs, and bonuses, so total compensation at top employers runs meaningfully higher than these numbers, especially in large tech for Backend and in semiconductors and defense for Embedded.

Q. How much do Backend Developer and Embedded Developer skills overlap?

About 20% (Jaccard similarity on each role's top-30 skills), one of the lowest overlaps between any two software-engineering titles we have compared. The shared core is small: Python, Git, debugging, automation, and a handful of process skills. Beyond that the stacks diverge sharply: Backend pulls toward AWS, Docker, Kubernetes, Java, microservices, and SQL; Embedded pulls toward C++, Linux, and prototyping.

Q. Which role has more job openings?

Backend Developer has roughly 2.79x more active postings: 7,234 versus 2,589 for Embedded Developer on the InterviewStack.io job board in May 2026, a difference of 4,645 listings. Backend roles span every company that runs a web service or API; Embedded roles concentrate in semiconductors, defense, aerospace, automotive, and consumer hardware, which is a structurally smaller pool.

Q. Which role is easier to enter at the junior level?

Embedded Developer has the wider entry door. About 6.7% of Embedded Developer postings are explicitly entry-level (173 of 2,589) versus 2.0% for Backend Developer (145 of 7,234), more than three times the proportional share. Backend overwhelmingly expects production-system experience, while Embedded teams are more willing to train new graduates with the right CS or electrical-engineering foundation.

Q. Should I become a Backend Developer or an Embedded Developer in 2026?

Pick Backend Developer if you want to build cloud services and APIs that scale to millions of users: Java, Python, AWS, microservices, and Kubernetes form the core stack, with the larger market and 11% higher median pay. Pick Embedded Developer if you want to write C++ that runs on physical devices like semiconductors, satellites, cars, drones, and medical devices, at companies such as NVIDIA, Anduril, SpaceX, and Marvell, where the work is closer to hardware and the entry door is materially wider.

Q. Which specific skills give the biggest salary premium in each role?

For Backend Developer, the highest-paying skills attach to systems and AI work: Rust ($179,500, +$29.5K above the $150K baseline), Generative AI and OpenAI ($175,000, +$25K), LLMs ($170,000, +$20K), Observability ($168,900, +$18.9K), and Distributed Systems ($165,000, +$15K). For Embedded Developer, the biggest premiums sit on the boundary with adjacent software domains: Computer Vision ($166,000, +$31.4K above the $134,600 baseline), Rust ($154,500, +$19.9K), and Machine Learning ($153,500, +$18.9K).

Q. Where are the jobs and how remote-friendly is each role?

Embedded Developer is sharply more US-concentrated: 53% of postings are in the US versus 17% for Backend Developer. Backend has a larger Indian presence (12% versus 8%) and a globally distributed long tail. Remote work splits the same way: 30% of Backend postings are remote and 51% are onsite, while only 8% of Embedded postings are remote and 77% are onsite. Embedded work needs lab access to physical hardware, which keeps it at the bench.

Bottom Line

Backend Developer and Embedded Developer share a name suffix, not a job. Backend is the cloud-services discipline: containerized Java and Python APIs, microservices on Kubernetes, distributed systems at scale, and a deep, global hiring market. Embedded is the hardware-adjacent discipline: C++ on microcontrollers and SoCs, Linux build systems, oscilloscopes and JTAG, and a US-anchored market dominated by semiconductor, defense, and aerospace employers. Backend pays 11% more at the median and has 2.79x the volume; Embedded has more than three times the proportional entry-level share and the wider door for new graduates. Browse live Backend Developer postings or Embedded Developer postings on the InterviewStack.io job board.

AI Engineer vs Machine Learning Engineer in 2026: Salary, Skills

Gnana — Sat, 16 May 2026 01:07:09 +0000

The Short Answer

Machine Learning Engineer pays more and hires more; AI Engineer is the wider entry door and the faster-growing title. Among US postings, the median ML Engineer base salary is $165,000 versus $145,000 for AI Engineer (a $20,000, 13.8% gap), and ML Engineer postings outnumber AI Engineer roles 4,781 to 4,091 on the InterviewStack.io job board in May 2026. The two skill sets share about 67% of their top-30 skills, so the real question is which third of the stack you specialize in: LLM-application engineering or production model engineering.

	AI Engineer	Machine Learning Engineer
Median US base salary	$145,000 (n=680)	$165,000 (n=1,087)
Active postings	4,091	4,781
Top skill	Python (68%)	Machine Learning (71%)
Entry-level share	5.8%	4.8%
Remote share	24%	28%
Skill overlap (Jaccard)	67%	67%

Key Findings

Median US base salary is $165,000 for ML Engineer (n=1,087) versus $145,000 for AI Engineer (n=680), a $20,000 (13.8%) gap.

ML Engineer has 4,781 active postings versus 4,091 for AI Engineer; about 1.17 ML Engineer roles for every AI Engineer role.

The two roles share 67% of their top-30 skill sets, one of the highest overlaps between any two AI/ML titles we have compared.

Neither role is entry-friendly: 5.8% of AI Engineer postings are entry-level (236 of 4,091) versus 4.8% for ML Engineer (230 of 4,781).

JAX ($204,000, n=87) and C++ ($186,000, n=119) carry the largest ML Engineer premiums; Distributed Systems ($183,200, n=40) leads for AI Engineer.

ML Engineer is more US-anchored (44% of postings versus 34%) and slightly more remote-friendly (28% versus 24%).

What Does Each Role Actually Do?

AI Engineer is an LLM application role. The work is wiring foundation models into shippable software: building retrieval pipelines on top of vector databases, calling LLM APIs from an application server, designing prompt and tool-use logic, and operating the resulting inference service. The exclusive-skill list (LangChain at 25%, OpenAI at 20%, Vector Databases at 18%, Embeddings at 13%, TypeScript at 12%) reads like a backend or full-stack engineer's resume with the LLM-application layer added on top. The output is usually a working feature in a product.

Machine Learning Engineer is a production model role. The week typically includes training, fine-tuning, and evaluating models, packaging them for deployment, and operating them at scale. The exclusive list (scikit-learn at 14%, Computer Vision at 13%, Apache Spark at 12%, Statistics at 11%, MLflow at 11%, Java at 10%) signals a broader model surface area that includes classical ML and computer vision, not only LLMs, with model-lifecycle tooling like MLflow tracking experiments end to end. Think of ML Engineer as the reliability engineer for models; AI Engineer as the product engineer for LLM features.

What Skills Do Both Roles Require?

Python anchors both stacks (68% for AI Engineer, 65% for ML Engineer), and Machine Learning itself shows up in 38% of AI Engineer postings versus 71% of ML Engineer ones. AWS sits at 34-36% in both, and the rest of the shared cluster (Monitoring, CI/CD, Generative AI, RAG, Azure, Google Cloud, APIs, Data Pipelines) keeps roughly two-thirds of the toolkit transferable in either direction.

Share of postings that ask for each skill, comparing AI Engineer (n=4,091) to Machine Learning Engineer (n=4,781). Skills shown are drawn from the union of each role's top set.

Several shared skills have asymmetric weight. PyTorch appears in 42% of ML Engineer postings but only 22% of AI Engineer postings, a clean signal that custom-model training is daily work for one role and occasional for the other; TensorFlow flips the same way (31% versus 17%). The signal inverts on the LLM side: RAG shows up in 39% of AI Engineer postings versus 16% of ML Engineer, and standalone LLM mentions roughly double in AI Engineer. Someone fluent in Python plus ML plus one cloud already has more than half the toolkit for either role.

Where Do the Roles Diverge?

Exclusive to AI Engineer

The AI Engineer side of the fork is dominated by LLM-application tooling and product-engineering surface area.

LangChain: 25%
OpenAI: 20%
Vector Databases: 18%
Embeddings: 13%
TypeScript: 12%
Agile: 12%

This cluster describes a job that lives in application servers, retrieval-augmented-generation pipelines, and inference endpoints. A posting that asks for LangChain plus vector databases is almost always describing a RAG system in production: the engineer builds the pipeline, deploys it, and keeps it running. The TypeScript signal is worth flagging too; it shows that a meaningful share of AI Engineer roles bleed into the application layer rather than staying on the backend. For the full per-role breakdown, see the AI Engineer skills deep dive.

Exclusive to Machine Learning Engineer

The ML Engineer side is dominated by classical ML, deep-learning specializations, and model-lifecycle tooling.

scikit-learn: 14%
Computer Vision: 13%
Apache Spark: 12%
Statistics: 11%
MLflow: 11%
Java: 10%

Statistics and scikit-learn signal that classical modeling is still core work, not a legacy concern. Computer Vision (13%) tells you a meaningful slice of ML Engineer postings come from autonomy, robotics, and visual-recognition teams (Waymo, Motional, NVIDIA, and General Motors all sit in the top hiring list). MLflow and Apache Spark together describe the production model lifecycle: experiments tracked and reproducible, training jobs scaled across a cluster, models packaged with their training context attached.

Which Pays More?

Among US postings, ML Engineer leads at a $165,000 median base salary (n=1,087) versus $145,000 for AI Engineer (n=680), a $20,000 (13.8%) gap. Salary numbers below are US-only base salary. Equity, RSUs, bonus, and sign-on are not disclosed in postings and are not in this dataset, so total compensation at top employers runs meaningfully higher than these figures, especially in tech and finance.

Median US base salary in USD for postings that mention each skill, restricted to US postings with structured salary data.

The premium is best read as a depth premium. ML Engineer postings consistently expect the candidate to have built and operated custom models, and the highest-paying skills reflect that. The top three: JAX at $204,000 (n=87, about $39K above baseline), C++ at $186,000 (n=119, about $21K above), and Transformers at $177,300 (n=87, about $12K above). All three reward low-level performance work or deep-learning depth. Computer Vision ($171,800, n=150) and PyTorch ($170,000, n=509) sit just above baseline as broader specializations.

For AI Engineer, the largest premiums attach to adjacent infrastructure and full-stack work, not to LLM tooling itself. Distributed Systems at $183,200 (n=40, about $38K above baseline) and Apache Spark at $170,000 (n=42, about $25K above) signal that the highest-paid AI Engineer roles are running inference at meaningful scale. React at $158,400 (n=48, about $13K above) confirms a real full-stack slice: the same engineer ships the application and the LLM behind it. Core production-tooling skills (Observability, MLOps, Scalability, FastAPI) cluster around $150,000, about $5K above the AI Engineer baseline.

The headline gap shrinks fast with specialization. A senior AI Engineer who can credibly own a distributed-inference platform earns at or above the ML Engineer median; a Transformers-fluent ML Engineer with a JAX background clears the AI Engineer median by a wide margin.

Which Has More Job Openings?

ML Engineer is the larger market by 690 postings (4,781 versus 4,091, a 1.17x ratio). The title has been around longer, the role is well-understood across industries, and most large companies already have an ML function. AI Engineer is the faster-growing newcomer, concentrated in companies actively shipping foundation-model-powered products.

Neither role is genuinely entry-friendly. 5.8% of AI Engineer postings are explicitly entry-level (236 listings), versus 4.8% for ML Engineer (230): roughly one entry-level AI Engineer role for every 17 postings, and one entry-level ML Engineer role for every 21. The senior-plus-staff share is 38% for AI Engineer and 42% for ML Engineer, so demand on either path is heavily concentrated in the upper half of the ladder.

Geography diverges meaningfully. ML Engineer is more US-anchored at 44% of postings versus 34% for AI Engineer; India sits as the second market for both at 13%. ML Engineer is also slightly more remote-friendly (28% remote, 30% hybrid, 52% onsite) than AI Engineer (24% remote, 33% hybrid, 51% onsite). Top ML Engineer employers skew toward product-tech and autonomy companies (Adobe, NVIDIA, Waymo, Spotify, General Motors); AI Engineer demand leans more toward consulting firms supporting enterprise rollouts (PricewaterhouseCoopers, Accenture) plus a long tail of LLM-first startups.

Which Should You Choose?

Choose AI Engineer if you:

Want to ship LLM-powered features in production: retrieval pipelines, vector stores, prompt and tool-use logic, inference APIs.
Already have backend, application, or full-stack engineering experience and want to add the LLM-application layer on top rather than learn deep learning from scratch.
Are willing to trade the higher median for the slightly wider entry door (5.8% versus 4.8%) and the steepest recent growth curve of any AI/ML title.

Choose Machine Learning Engineer if you:

Want to train, fine-tune, evaluate, and operate models, including classical ML, deep learning, and computer vision systems, not only LLM applications.
Have or want to build research depth: PyTorch, Transformers, MLflow, Apache Spark, statistics, and ideally low-level performance work (JAX, C++) where the salary curve climbs fastest.
Care about market breadth: 17% more openings, a higher US share (44% versus 34%), and a more remote-friendly mix.

If the choice still isn't clean, the shared 67% is your hedge. Build Python plus ML plus one cloud plus one of the deep-learning frameworks (PyTorch is the safer pick: 42% of ML Engineer postings, 22% of AI Engineer postings) and let the work you find yourself drawn to make the decision. Our interactive courses cover the foundations across Python, ML, and system design, the question bank lets you drill ML, statistics, and distributed-systems topics one at a time, and AI mock interviews put you under realistic conditions for either track.

FAQ

Q. What's the salary difference between AI Engineer and Machine Learning Engineer in 2026?

The median US base salary is $165,000 for Machine Learning Engineer (n=1,087) versus $145,000 for AI Engineer (n=680), a $20,000 (13.8%) premium for the ML Engineer role. Both figures are base only and exclude equity, RSUs, and bonuses, so total compensation at top employers runs meaningfully higher for either path.

Q. How much do AI Engineer and Machine Learning Engineer skills overlap?

About 67% (Jaccard similarity on each role's top-30 skills), one of the highest overlaps between any two distinct AI/ML titles. Python, Machine Learning, AWS, LLMs, Generative AI, PyTorch, TensorFlow, and major clouds appear in both stacks. The remaining third is where the roles fork: AI Engineer pulls toward LangChain, OpenAI, vector databases, and embeddings; ML Engineer pulls toward scikit-learn, computer vision, Apache Spark, MLflow, and statistics.

Q. Which role has more job openings?

Machine Learning Engineer has roughly 1.17x more active postings: 4,781 versus 4,091 for AI Engineer on the InterviewStack.io job board in May 2026, a difference of 690 listings. The ML Engineer market is older and more established; AI Engineer is the newer title that emerged with the LLM-application boom and is still ramping in absolute volume.

Q. Which role is easier to enter at the junior level?

Neither is genuinely entry-friendly. About 5.8% of AI Engineer postings are explicitly entry-level (236 of 4,091) versus 4.8% for ML Engineer (230 of 4,781). AI Engineer has the slightly wider door, but both roles overwhelmingly expect production experience: ML Engineer wants shipped models in production, AI Engineer wants shipped LLM-powered applications.

Q. Should I become an AI Engineer or a Machine Learning Engineer in 2026?

Pick AI Engineer if you want to ship LLM-powered features in production: retrieval pipelines, vector stores, prompt and tool-use logic, inference APIs. Pick Machine Learning Engineer if you want to train, tune, and operate custom models, including deep learning and computer vision systems. ML Engineer pays a $20K higher median and has more openings; AI Engineer has the wider entry door and a steeper recent growth curve.

Q. Which specific skills give the biggest salary premium in each role?

For Machine Learning Engineer, the highest-paying skills sit in low-level performance and deep-learning specializations: JAX ($204,000, +$39K above the $165K baseline), C++ ($186,000, +$21K), Transformers ($177,300, +$12K), and Computer Vision ($171,800, +$7K). For AI Engineer, the biggest premiums attach to adjacent infrastructure and full-stack work: Distributed Systems ($183,200, +$38K above the $145K baseline) and Apache Spark ($170,000, +$25K), with React ($158,400, +$13K) signaling that AI Engineer is sometimes a full-stack title.

Q. Where are the jobs and how remote-friendly is each role?

ML Engineer is meaningfully more US-concentrated (44% of postings versus 34% for AI Engineer); India is the second-largest market for both at about 13%. ML Engineer is also slightly more remote-friendly: 28% remote versus 24% for AI Engineer. Both roles are dominated by onsite work (52% and 51%) with hybrid in the 30-33% range.

Bottom Line

ML Engineer is the larger, higher-paying, more model-depth-focused half of the modern AI hiring market. AI Engineer is the faster-growing, slightly more accessible, more LLM-application-flavored half, with a meaningful full-stack slice. The two skill sets share two-thirds of their tooling, so the choice is really a choice about which third you want to specialize in: deep learning, computer vision, and the model lifecycle on one side; LLM applications, retrieval pipelines, and production inference on the other. Browse live AI Engineer postings or Machine Learning Engineer postings on the InterviewStack.io job board, or read the deeper AI Engineer skills analysis for the full per-role breakdown.

Applied Scientist Skills Companies Want in 2026: A comprehensive analysis on 3,146 active postings

Gnana — Fri, 15 May 2026 00:55:52 +0000

The Applied Scientist Title Hides Two Very Different Roles

"Applied Scientist" reads like a single job title, but it isn't. Inside the same keyword sit at least two distinct roles: the product-science flavor (experimentation, causal inference, A/B testing, recommendation systems) that lives at consumer tech companies, and the research-lab flavor (biostatistics, clinical research, biotech R&D, applied physics) that lives at universities, hospitals, and pharma. In the live market, the second flavor is more common than most candidates expect.

To put numbers on it, we looked at every active Applied Scientist posting on the InterviewStack.io job board as of May 2026: 3,146 listings, with skills extracted from descriptions and synonyms collapsed (so ETL and data pipelines count once, GCP and Google Cloud count once).

The most distinctive structural feature of the role: no single skill clears the 50% line. The Applied Scientist title is fragmented enough that the most common individual skill, A/B Testing, appears in only 26.3% of postings. Compare that to Data Engineer, where three skills cluster around 71-74%. There is no canonical Applied Scientist stack in the way there is a canonical Data Engineer stack.

Key Findings

3,146 active Applied Scientist postings analyzed across the live job board as of May 2026.

No table-stakes tier exists: the most-requested skill, A/B Testing, appears in only 26.3% of postings (828 of 3,146). Python (25.4%) and Statistics (24.6%) follow.

Statistics & Experimentation is the dominant skill family at 44.6% of postings, ahead of Coding Languages (28.3%) and Machine Learning & AI (19.3%).

Median US base salary is $110,000 across 878 postings with US salary disclosed; equity, bonus, and sign-on are not in the data.

Deep-learning specialists earn $145,300 in median US base salary (PyTorch and Deep Learning both n=60+), about $35K above the role baseline.

Mid-level dominates at 60.6% (1,905 postings); entry-level is 14.2% (446), markedly more accessible than Data Engineer's 3%.

60.9% of postings are in the US, with Singapore (6.0%), the UK (5.2%), Canada (4.8%), and India (3.9%) rounding out the next tier.

Onsite is the dominant work mode at 77.1% of postings; remote is just 9.9%, reflecting the heavy academia, healthcare, and pharma presence in the employer mix.

What Skill Families Define an Applied Scientist Role in 2026?

Group every individual skill into the higher-level family it belongs to and count how many postings ask for at least one skill in that family. The shape of the role becomes a fan of related specialties rather than a single stack.

Share of Applied Scientist postings that ask for at least one skill in each family. A posting that mentions both A/B Testing and Statistics counts once under "Statistics & Experimentation".

The families that actually define the role:

Statistics & Experimentation: 44.6% (A/B testing, statistical inference, forecasting)
Coding Languages: 28.3% (overwhelmingly Python; TypeScript is a long-tail noise term)
Tools & Infrastructure: 21.5% (monitoring of deployed models, experiment automation)
Machine Learning & AI: 19.3% (classical ML, deep learning, PyTorch, LLMs, generative AI)
Spreadsheets: 14.1% (essentially Excel, mostly in clinical and life-sciences postings)
Data Visualization & BI: 10.0% (generic visualization, plus Tableau and Power BI as a long tail)
Data Engineering Foundations: 9.1% (data quality, data pipelines)
Querying & SQL: 5.9% (almost entirely SQL itself)
Cloud Platforms: 5.5% (Google Cloud and AWS roughly tied)

A few things stand out against Data Engineer and AI Engineer postings. Statistics & Experimentation, which sits at 17% for Data Engineer, leads the Applied Scientist field at 44.6%; this is the single biggest differentiator from neighboring roles. Querying & SQL, which dominates analyst and engineer hiring, sits at just 5.9% for Applied Scientist, the lowest of any role we have analyzed. And Spreadsheets at 14.1% reflects how much of the hiring comes from clinical research, biostatistics, and lab-applied-science postings where Excel is still a primary analytics tool.

What Are the Three Tiers of Individual Applied Scientist Skills?

Drill into individual skills and three tiers appear, with one important caveat: the top tier is empty.

Top individual skills in Applied Scientist postings, by share of listings that mention them. Skills above 50% would be table stakes; 20-50% are common; 5-20% are differentiators. Generic role keywords and universal soft skills are filtered before counting.

Table Stakes (50%+ of postings)

There are none. No individual skill appears in more than half of Applied Scientist postings. The role is structurally too fragmented across product-science, research, and ML-building subspecialties for any one skill to be universal. This is the single most useful framing for a candidate: do not waste time trying to "cover everything." Pick a flavor of the role and concentrate.

Common Expectations (20-50% of postings)

Three skills cluster in the common tier, and they are exactly the three you would expect from an experimentation-oriented role:

A/B Testing: 26.3%
Python: 25.4% (Applied Scientist + Python openings)
Statistics: 24.6% (Applied Scientist + Statistics openings)

The three travel together. Python plus Statistics co-occur in 369 postings (11.7% of the market, lift 1.87), and A/B Testing plus Statistics co-occur in 264 postings (8.4%, lift 1.29). A candidate competent in all three is positioned for the experimentation-heavy product-science version of the role, which is the most consistently defined flavor in the dataset.

Differentiators (5-20% of postings)

This tier is where Applied Scientist subspecialties separate.

Machine Learning: 15.3% (Applied Scientist + Machine Learning openings)
Excel: 14.0%
Monitoring: 11.0%
Data Visualization: 8.7%
Automation: 8.0%
SQL: 5.7%
Deep Learning: 5.6%
PyTorch: 5.4% (Applied Scientist + PyTorch openings)

Three groupings sit inside this tier. Machine Learning, Deep Learning, and PyTorch (5-15%) are the model-building flavor of the role. Excel and SQL are the analytics-and-reporting flavor (notably, SQL is unusually low for a role family adjacent to data analytics, which tells you most Applied Scientist work happens in Python notebooks on extracted data, not directly in a warehouse). Monitoring and Automation are infrastructure-leaning differentiators for postings that ask the scientist to ship and operate models, not just train them.

Of the newer AI-stack terms, only PyTorch (5.4%) clears into the differentiator tier; LLMs (4.5%) and Generative AI (3.6%) still sit below the 5% cutoff in noise territory, though both are rising fast (a year ago all three were well below noise).

Which Applied Scientist Skills Pay More Than the Baseline?

Salary numbers below are restricted to US postings only (where wage-transparency laws produce consistent disclosure) so they are directly comparable. The numbers are base salary: equity, bonuses, RSUs, and sign-on are not disclosed in postings, so total compensation at top employers is meaningfully higher than what we report here, especially in product-led tech.

The overall median US base salary for Applied Scientist postings is $110,000 (n=878). That sits below the Data Engineer median ($128,300) and below the AI Engineer median ($146,000), and the reason is in the employer mix: 38% of postings are in healthcare, education, biotech, or pharmaceutical industries, where base salaries are lower than they are in product-led tech. The Big-Tech Applied Scientist roles you might be picturing exist, but they are a slice of the market, not the bulk of it.

Median US base salary in USD for postings that mention each skill, among US Applied Scientist postings with structured salary data.

The skills with the largest premiums above the $110,000 baseline cluster around C++ and the deep-learning/modern-AI stack.

Premiums of roughly $30K to $36K:

C++: $145,900 (n=25), about $35,900 above baseline
PyTorch: $145,300 (n=62), about $35,300 above baseline
Deep Learning: $145,300 (n=60), about $35,300 above baseline
Data Pipelines: $140,000 (n=29), about $30,000 above baseline
Generative AI: $140,000 (n=51), about $30,000 above baseline

Premiums of roughly $20K to $30K:

LLMs: $139,600 (n=62), about $29,600 above baseline
Machine Learning: $138,600 (n=169), about $28,600 above baseline
Agile: $130,200 (n=34), about $20,200 above baseline

Premiums of roughly $10K to $20K:

AWS: $128,000 (n=49), about $18,000 above baseline
Java: $125,100 (n=27), about $15,100 above baseline
Google Cloud: $124,500 (n=34), about $14,500 above baseline
Python: $121,500 (n=257), about $11,500 above baseline
Forecasting: $120,000 (n=45), about $10,000 above baseline

Skills near baseline (under $5K above):

Statistics: $112,600 (n=273), about $2,600 above baseline
SQL: $112,100 (n=69), about $2,100 above baseline
A/B Testing: $110,000 (n=297), at baseline

And finally, skills that sit below the role baseline:

Data Visualization: $96,200 (n=76), about $13,800 below baseline
Monitoring: $95,500 (n=101), about $14,500 below baseline
Excel: $85,000 (n=133), about $25,000 below baseline
Power BI: $74,400 (n=26), about $35,600 below baseline

The below-baseline pattern is informative, not noise. Excel, Power BI, and generic data visualization show up most often in clinical research, university lab, and healthcare Applied Scientist postings, where base salaries are structurally lower than in product-led tech. Picking up Excel skills does not lower your salary; it correlates with the segment of the market that pays less. Read the median for what it is: a marker of which kind of Applied Scientist posting tends to mention each skill.

The practical takeaway: the experimentation-and-statistics version of the role pays roughly at baseline, the model-building version pays a $20K to $35K premium, and the research-and-reporting version sits below baseline. Pick the version you want to interview for, and let your skill mix match it.

What Is the Dominant Applied Scientist Skill Stack?

We computed every two-skill co-occurrence among the top 25 skills to find the combinations that show up together more often than chance. Two distinct stacks emerge.

Skill pair	Postings that mention both	% of postings	Lift
Deep Learning + PyTorch	95	3.0%	10.11
Deep Learning + Machine Learning	147	4.7%	5.48
Machine Learning + PyTorch	138	4.4%	5.33
LLMs + Machine Learning	103	3.3%	4.70
Python + PyTorch	159	5.1%	3.70
AWS + Python	88	2.8%	3.50
Python + SQL	155	4.9%	3.41
Deep Learning + Python	148	4.7%	3.33
Machine Learning + Python	350	11.1%	2.86
SQL + Statistics	104	3.3%	2.36
Python + Statistics	369	11.7%	1.87
Automation + Machine Learning	76	2.4%	1.98
Machine Learning + Statistics	230	7.3%	1.94
A/B Testing + Machine Learning	177	5.6%	1.40
A/B Testing + Statistics	264	8.4%	1.29

The story is two stacks layered over the role:

The broad experimentation stack is Python plus Statistics, the highest-volume pair at 369 postings (11.7% of the market, lift 1.87). Add A/B Testing as a third leg (264 postings with Statistics, lift 1.29) and you have the canonical product-science Applied Scientist: someone who designs experiments, runs hypothesis tests, and writes analysis in Python notebooks. This is the most consistently defined version of the role.
The deep-learning specialty stack is Machine Learning plus Python (350 postings, 11.1%, lift 2.86), with a sharp PyTorch plus Deep Learning sub-pair (95 postings, lift 10.11). Lift above 10 is rare in any dataset: it means PyTorch and Deep Learning postings overlap nearly 10 times more than their individual frequencies would predict, because they are essentially the same skill in this market. Add LLMs or Generative AI on top and you have the modern-AI Applied Scientist building, fine-tuning, or evaluating models.

The two stacks barely overlap. Postings that lead with A/B Testing rarely also ask for PyTorch; postings that ask for PyTorch rarely also ask for A/B Testing. Choosing which stack to interview for is the most important upstream decision a candidate can make.

Who's Hiring at Which Seniority Level?

We tagged each posting's seniority based on title keywords (Senior, Lead, Principal, Junior, Intern). Postings with no explicit signal default to mid-level.

Seniority distribution of Applied Scientist postings.

Mid-level: 60.6% (1,905 postings)
Senior: 16.1% (508) (senior Applied Scientist openings)
Entry: 14.2% (446) (entry-level Applied Scientist openings)
Staff / Lead / Principal: 9.1% (287)

Two things stand out. First, the entry-level door is much wider here than for adjacent roles. 14.2% of Applied Scientist postings are explicitly entry-level, compared with 3% for Data Engineer and roughly 8% for Data Analyst. The reason is the academia and healthcare share of the employer mix: universities and research hospitals routinely hire entry-level scientists with newly minted PhDs (or, increasingly, master's degrees in statistics, biostatistics, or applied math). If you are a PhD student or postdoc looking for a first industry role, Applied Scientist is one of the more open entry points in the role family.

Second, the senior-and-above slice (senior plus staff) is 25.3% of the market, lighter than Data Engineer (45%) and AI Engineer (40%). The IC ladder in research-flavored Applied Scientist roles is real but narrower; longer-term career growth often routes through Principal Investigator, ML Manager, or Research Director titles rather than Staff-IC tracks.

Where Are Applied Scientist Jobs Located, and How Remote-Friendly Are They?

Geography is the most US-concentrated of any data-and-analytics role we have analyzed. The US share is over 60%, with no other country breaking 7%.

Top countries by share of Applied Scientist postings.

United States: 60.9% (1,916) (US-only Applied Scientist openings)
Singapore: 6.0% (188)
United Kingdom: 5.2% (163)
Canada: 4.8% (150)
India: 3.9% (123)
Germany: 2.0% (63)
China: 1.6% (50)
Australia: 1.3% (40)

Two of those numbers are unusual. Singapore at 6.0% is the second-largest single market for Applied Scientists, driven primarily by Nanyang Technological University's heavy posting volume in this role family. India at 3.9% is much lower than for Data Engineer (where India is 23%), because the global consulting-and-services firms that drive India's Data Engineer demand don't hire as many Applied Scientists; the work is concentrated at university research labs and pharma R&D centers, which are based in the US and Western Europe.

Work mode reinforces the same pattern.

Share of Applied Scientist postings tagged with each work mode. Some postings carry multiple tags, so percentages sum to more than 100%.

Onsite: 77.1% of postings (2,427)
Hybrid: 19.4% (611)
Remote: 9.9% (310) (fully-remote Applied Scientist openings)

77% onsite is the highest onsite share of any role we have analyzed; for context, Data Engineer is ~50% onsite and Data Analyst is ~56%. The cause is the employer mix: universities, hospitals, pharma R&D, and government labs almost never post remote scientist roles. They want the work happening in their facilities, often because the data is sensitive, the equipment is physical, or the IRB protocols require it. The fully remote slice exists, but it concentrates in product-led tech companies (Adobe and a small handful of others on this list), not in the academic-and-pharma majority.

Who's Hiring Applied Scientists in 2026?

The list of top hiring employers is one of the most informative single signals in this dataset. It looks almost nothing like the top employers for Data Engineer or AI Engineer.

Top companies by active Applied Scientist postings. Counts include all locations of the same job.

Nanyang Technological University: 155 postings (research university)
Thermo Fisher Scientific: 59 (life-sciences instruments and services)
Mass General Brigham: 52 (academic medical center)
Adobe Inc.: 46 (consumer software)
Washington University in St. Louis: 45 (research university)
University of Arizona: 43 (research university)
AstraZeneca: 40 (pharmaceutical)
Eurofins Scientific: 31 (lab testing and life sciences)
Danaher Corporation: 31 (life-sciences and diagnostics conglomerate)
Merck & Co., Inc.: 26 (pharmaceutical)
Mayo Clinic: 26 (academic medical center)
Eli Lilly and Company: 25 (pharmaceutical)

The top 12 employers are dominated by research universities (Nanyang, Washington University, University of Arizona, plus several more outside the top 12), academic medical centers (Mass General, Mayo Clinic, Cleveland Clinic), pharmaceutical firms (AstraZeneca, Merck, Eli Lilly, Amgen), and life-sciences companies (Thermo Fisher, Danaher, Eurofins). Adobe is the only consumer-tech name in the top tier. The Big-Tech Applied Scientist roles that dominate the role's reputation (at Amazon, Microsoft, Meta) exist on the board but are spread across many smaller per-company posting counts, so they do not surface in the top-12 list.

If you are interviewing for an Applied Scientist role in 2026, the practical implication is this: the modal employer is a research university, hospital, or pharma R&D group, not a Big-Tech ML team. Tailor your resume, your research statement, and your interview prep accordingly. Our interview preparation guides cover the technical and behavioral rounds at the specific companies above.

How to Use This in Your Job Search

If you are preparing for an Applied Scientist job hunt, the data points to a clear sequence.

1. Pick a flavor of the role before applying. Applied Scientist is two roles inside one keyword: the product-science version (experimentation, A/B testing, statistics, Python) and the model-building version (Machine Learning, Deep Learning, PyTorch, increasingly LLMs and Generative AI). The skills, employer types, salary distributions, and interview formats are different. A resume that tries to be both reads as expert in neither. Decide which version you are targeting and concentrate your prep there.

2. Build the matching foundation. For the product-science flavor, the foundation is Python plus Statistics plus A/B Testing methodology: confidence intervals, hypothesis testing, multiple-comparison correction, causal-inference patterns. For the model-building flavor, the foundation is Python plus PyTorch plus the math behind modern deep learning (linear algebra, optimization, attention mechanisms). The salary data shows the model-building track pays roughly $28K to $35K more in median US base, but it has a steeper technical entry bar and a tighter employer set.

3. Add the differentiator your target stack values. For product-science, add forecasting (+$10K), Bayesian methods, or a strong causal-inference toolkit. For model-building, add a current modern-AI specialty: LLMs ($139,600), Generative AI ($140,000), or distributed training. Cloud fluency (AWS at $128,000, Google Cloud at $124,500) lifts both stacks roughly $14K to $18K above the role baseline.

4. Drill the topics, then practice the rounds. Reading about Applied Scientist skills is easy; performing under interview conditions is the hard part. Our interview-prep courses cover the foundations across statistics, ML, system design, and SQL. The question bank lets you drill statistics, A/B testing, machine learning, and deep-learning topics one at a time. AI mock interviews let you practice the full round under realistic conditions, with on-demand feedback on case studies, experimental design, and ML system design.

5. Filter the job board for your flavor. Browse current Applied Scientist openings on the InterviewStack.io job board and combine role and skill filters to narrow to the version you want, e.g., Applied Scientist + Statistics for the experimentation track or Applied Scientist + PyTorch for the deep-learning track. The board updates daily, so the listings are current.

FAQ

Q. What skills do companies want for Applied Scientist roles in 2026?

No single skill clears a majority of postings. The most-requested individual skill, A/B Testing, appears in 26.3% of listings, followed by Python (25.4%) and Statistics (24.6%). At the family level, Statistics & Experimentation leads at 44.6%, followed by Coding Languages (28.3%) and Machine Learning & AI (19.3%). Differentiators like Machine Learning (15.3%), PyTorch (5.4%), and Deep Learning (5.6%) pay the largest salary premiums.

Q. What is the median Applied Scientist salary in 2026?

The median US base salary across 878 Applied Scientist postings with disclosed US salary is $110,000. That figure excludes equity, bonuses, and sign-on, so total compensation at top employers runs meaningfully higher. Postings that ask for PyTorch, Deep Learning, LLMs, or Generative AI cluster around $139K to $145K, roughly $30K to $35K above the role baseline.

Q. Which Applied Scientist skills pay the highest premium over the role baseline?

Among US postings, C++ and the deep-learning/modern-AI stack pay the most. C++ ($145,900, n=25), PyTorch ($145,300, n=62), and Deep Learning ($145,300, n=60) top the list, followed by Data Pipelines ($140,000, n=29), Generative AI ($140,000, n=51), and LLMs ($139,600, n=62), each sitting roughly $30K to $36K above the $110,000 role baseline. Machine Learning ($138,600, n=169) and AWS ($128,000, n=49) follow at $19K to $29K premiums.

Q. Is Applied Scientist a good entry-level role to break into?

It is more accessible than several adjacent roles. 14.2% of Applied Scientist postings are explicitly entry-level (446 of 3,146), well above the 3% entry share for Data Engineer. Mid-level postings dominate at 60.6%, and senior plus staff together are 25.3% of the market.

Q. Where are Applied Scientist jobs located, and how remote-friendly are they?

The United States is by far the largest market at 60.9% of postings (1,916 of 3,146). The next-largest single markets are Singapore (6.0%), the United Kingdom (5.2%), Canada (4.8%), and India (3.9%). Work mode is heavily onsite at 77.1% of postings, with 19.4% hybrid and just 9.9% remote. Many top employers are universities, hospitals, and pharma R&D centers, which rarely post remote scientist roles.

Q. Which companies hire the most Applied Scientists in 2026?

Nanyang Technological University leads with 155 active postings, followed by Thermo Fisher Scientific (59), Mass General Brigham (52), Adobe (46), Washington University in St. Louis (45), University of Arizona (43), AstraZeneca (40), Eurofins Scientific (31), Danaher (31), Merck (26), Mayo Clinic (26), and Eli Lilly (25). The top of the list is dominated by universities, hospitals, and life-sciences companies rather than Big Tech.

Q. What is the dominant Applied Scientist skill stack in 2026?

Two stacks coexist in the data. The broad analytical stack is Python plus Statistics, which appear together in 369 postings (11.7% of the market, lift 1.87), often with A/B Testing as a third leg. The deep-learning specialty stack is Machine Learning plus Python (350 postings, lift 2.86) with a tight PyTorch plus Deep Learning sub-pair (95 postings, lift 10.11). The split reflects two distinct flavors of the role: experimentation-heavy product science and model-building research.

Final Thoughts

The Applied Scientist role in 2026 is the most fragmented title in the data-and-analytics family. No single skill carries the role, no single industry dominates the employer mix, and no single salary band describes the comp range. What does carry the role is the deliberate choice of which flavor to interview for: experimentation and statistics, or model-building and deep learning. Pick one early, build the foundation cleanly, and the differentiator that earns the salary premium will follow.

We will refresh this analysis quarterly so the trend lines stay current.

Data Analyst vs Data Scientist 2026: Skills, Salary, Hiring

Gnana — Thu, 14 May 2026 01:18:58 +0000

Are Data Analyst and Data Scientist Still the Same Job in 2026?

From the outside, the two roles look interchangeable: similar posting volumes, similar geographies, similar work-mode mix. From the inside, they are two different jobs that happen to share a top-skill list. The Data Analyst sits next to the business and explains what happened with SQL and a dashboard; the Data Scientist sits next to the product and explains what is likely to happen with a model.

We compared every active Data Analyst posting (6,485 listings) with every active Data Scientist posting (6,087 listings) on the InterviewStack.io job board as of May 2026, with skills extracted from descriptions and synonyms collapsed. The takeaway is sharper than the headline overlap suggests: roughly half the skills appear in both lists, but the salary, the modeling stack, and the senior-career ceiling all push decisively toward Data Scientist.

Key Findings

Volume is essentially tied: 6,485 Data Analyst postings vs 6,087 Data Scientist postings (ratio 1.07).

Median US base salary gap is $32,300: $95,000 for Data Analyst (n=1,376) vs $127,300 for Data Scientist (n=1,370), a 25% premium for Data Scientist.

Skill overlap is moderate: Jaccard 0.46 on top-30 skill sets, so roughly half of each role's skill profile transfers.

The lead skill flips: SQL leads Data Analyst (60% of postings) while Python leads Data Scientist (64%).

Modeling stack is exclusive to Data Scientist: Generative AI (14%), LLMs (14%), TensorFlow (13%), PyTorch (13%), scikit-learn (11%), Deep Learning (10%), and NLP (10%) clear our exclusivity threshold.

BI stack tilts toward Data Analyst: Tableau (32% vs 14%), Power BI (31% vs 14%), and Excel (33% vs 11%) are 2 to 3 times more common in Data Analyst postings.

Staff ceiling is nearly 2x higher for Data Scientist: 13% of Data Scientist postings are staff-level, vs 7% for Data Analyst.

Geography and work mode are near-identical: US 39% in both, fully-remote share 22% vs 21%.

At a Glance: How Do the Two Roles Compare?

Dimension	Data Analyst	Data Scientist
Active postings	6,485	6,087
Median US base salary	$95,000 (n=1,376)	$127,300 (n=1,370)
Lead skill	SQL (60%)	Python (64%)
Skill profile overlap (Jaccard)	46%	46%
Entry-level share	8%	9%
Staff+ share	7%	13%
Fully-remote share	22%	21%
Largest country share	US (39%)	US (39%)
Top exclusive skills	Looker, Data Modeling	Generative AI, LLMs, PyTorch, TensorFlow

The table is the comparison in one frame: volumes match, geography matches, work mode matches. What does not match is pay, the senior ceiling, and which half of the modeling-and-BI continuum the role lives on.

How Similar Are the Two Skill Profiles?

Compute Jaccard similarity across each role's top-30 skill list and the answer is 0.46, meaning roughly half the skill set transfers. That is high enough that a Data Analyst with strong Python and Statistics can credibly retool toward Data Science, but low enough that the resumes are not interchangeable.

Share of postings that mention each top-shared skill, Data Analyst (emerald) vs Data Scientist (blue). Each bar is the percentage of that role's postings that name the skill.

The most telling part of the chart is how the lead skill flips. SQL is the most-demanded skill in Data Analyst postings (60% of listings), but only the third-most for Data Scientist (45%). Python is the inverse: it leads Data Scientist at 64% but trails to third place in Data Analyst at 44%. The two roles share a vocabulary; they emphasize different syllables.

Machine Learning is the cleanest divider. It appears in 49% of Data Scientist postings and only 11% of Data Analyst postings, the widest gap among shared skills. Statistics tells the same story at smaller scale (37% vs 22%). A Data Analyst posting that asks for ML is usually a hybrid analyst-scientist role; a Data Scientist posting without ML is rare.

Which Skills Does Each Role Own Outright?

Beyond the shared list, each role has skills the other almost never asks for. We define "exclusive" as: appears in at least 8% of one role's postings and fewer than 5% of the other's.

Exclusive to Data Scientist (the modeling stack):

Generative AI: 14%
LLMs: 14%
Apache Spark: 13%
TensorFlow: 13%
pandas: 13%
PyTorch: 13%
Google Cloud: 12%
scikit-learn: 11%
Deep Learning: 10%
NLP: 10%

Exclusive to Data Analyst (BI and modeling-for-warehousing):

Looker: 12%
Data Modeling: 9%

The asymmetry is striking. Ten skills clear the exclusivity bar for Data Scientist, only two for Data Analyst. That is the practical meaning of the salary gap: Data Scientist roles ask for a longer, more specialized list of model-building tools, and pay accordingly.

Two other patterns worth naming. First, the BI stack (Tableau, Power BI, Excel) does not clear the exclusivity threshold because Data Scientist postings still ask for it 11% to 14% of the time, but it is 2 to 3 times more common in Data Analyst postings. If your strength is dashboards and stakeholder-facing visualization, Data Analyst openings that ask for Tableau or Power BI are the higher-density target. Second, cloud platforms (AWS, Azure, Google Cloud) cluster on the Data Scientist side, reflecting that production model deployment requires more cloud surface area than building a dashboard does.

How Much More Do Data Scientists Earn?

Salary numbers below are restricted to US postings only (where wage-transparency laws produce consistent disclosure) so they are directly comparable. They are base salary: equity, bonuses, RSUs, and sign-on are not disclosed in postings, so total compensation at top employers runs meaningfully higher than what we report here, especially in tech and finance.

The median US base salary for Data Analyst postings is $95,000 (n=1,376). For Data Scientist postings it is $127,300 (n=1,370). That is a $32,300 gap, roughly 25% higher for Data Scientist.

Median US base salary for postings that mention each skill, by role. The skill-level comparison shows the gap is structural, not just a mix-shift toward different skills.

The more revealing test is to hold the skill constant and look at salary by role:

Skill	Data Analyst median	Data Scientist median	Premium
SQL	$100,000 (n=850)	$131,200 (n=701)	+$31,200
Python	$100,000 (n=606)	$128,000 (n=948)	+$28,000
Statistics	$98,200 (n=371)	$133,100 (n=633)	+$34,900
Machine Learning	$97,100 (n=154)	$125,100 (n=762)	+$28,000
Snowflake	$115,000 (n=173)	$137,500 (n=151)	+$22,500
Tableau	$99,500 (n=503)	$113,500 (n=210)	+$14,000

At every shared skill, the Data Scientist title carries a premium of roughly $14K to $35K. The conclusion: the salary gap is not just a mix-shift (more cloud and ML on the Data Scientist side); it is a real, role-level premium. The same SQL pays $31K more in a Data Scientist posting than in a Data Analyst one.

Is One Role More Entry-Friendly Than the Other?

Both roles look similar at the entry door but diverge sharply at the top of the ladder.

Seniority	Data Analyst	Data Scientist
Entry	8% (499)	9% (550)
Mid-level	61% (3,940)	54% (3,257)
Senior	25% (1,592)	24% (1,461)
Staff / Lead / Principal	7% (454)	13% (819)

Entry-level openings are slightly more common in Data Scientist postings (9% vs 8%), which surprises most people: the conventional wisdom that Data Analyst is the "easier" first job into data does not show up in our hiring data. What the data does show is that the staff-and-above ceiling is nearly twice as high for Data Scientist (13% vs 7%). If you are picking a role for long-term career trajectory, Data Science has more senior IC runway above the mid-level band.

The mid-level concentration is also worth flagging. 61% of Data Analyst postings are mid-level, compared with 54% for Data Scientist, so Data Analyst hiring is more bunched in the middle of the experience curve. A senior-leaning candidate may find a deeper Data Scientist market for senior roles and a shallower one for senior Data Analyst.

Where Are the Jobs, and How Remote-Friendly Is Each Role?

Geography and work mode are the dimensions where the two roles look nearly identical, so neither should be the tiebreaker.

Geography (top 5):

Country	Data Analyst	Data Scientist
United States	39%	39%
India	11%	12%
United Kingdom	5%	5%
Canada	5%	4%
Germany	3%	3%

Work mode (postings can carry multiple tags, so percentages sum to more than 100%):

Mode	Data Analyst	Data Scientist
Onsite	57%	57%
Hybrid	33%	32%
Remote	22%	21%

The labor markets that hire one role hire the other; the work-mode default for both is onsite, with hybrid as a strong second. If fully-remote work is the priority, fully-remote Data Scientist openings and fully-remote Data Analyst openings exist in similar proportions, but neither role is remote-first in 2026.

How Should You Choose Between Data Analyst and Data Scientist?

The data points to a clean decision frame.

Pick Data Analyst if you want a faster on-ramp, prefer working close to business stakeholders with SQL and a BI tool (Tableau, Power BI, Looker), and are comfortable trading the salary ceiling for a more stakeholder-facing day-to-day. The skills compound into senior analyst, analytics engineer, and BI lead roles. Mid-level dominates the postings (61%), so once you clear the entry-level door, demand is broad.

Pick Data Scientist if you are willing to invest in machine learning, statistics, and the Python ML stack (PyTorch, TensorFlow, scikit-learn, plus increasingly MLOps and LLM tooling). You get a $32K higher median, a longer staff-level ladder (13% of postings are staff vs 7%), and direct exposure to the Generative AI and LLM work that is reshaping the role. The trade is a higher technical floor and more cloud and model-deployment surface area.

The hybrid path is real: the Data Engineer route sits adjacent to both, with its own salary premium and a different stack focus. The cleanest skill ladder from analyst to scientist is to layer Python plus Statistics plus one of scikit-learn/PyTorch onto a strong SQL foundation, then move into a Data Scientist or hybrid analyst-scientist role.

Whichever side you target, our interactive courses cover the foundations across SQL, Python, statistics, and ML, and the question bank lets you drill the specific topics that come up in onsite rounds. For the final mile, AI mock interviews reproduce the analytics case study or ML system design conversation you will actually face. When you are ready to apply, filter Data Analyst openings or Data Scientist openings to your stack.

FAQ

Q. Is Data Scientist a better-paid role than Data Analyst in 2026?

Yes. The median US base salary for Data Scientist postings is $127,300 (n=1,370), versus $95,000 for Data Analyst postings (n=1,376). That is a $32,300 gap, roughly 25% higher for Data Scientist roles. The gap is structural, not skill-explained: at the same listed skill (SQL, Python, Statistics), Data Scientist postings pay $28K to $35K more than Data Analyst postings.

Q. How much do Data Analyst and Data Scientist skills overlap?

About half. The Jaccard overlap on each role's top-30 skill list is 0.46, meaning roughly half of the skills demanded in one role are also demanded in the other. SQL, Python, Statistics, and Machine Learning appear in both, but with very different intensities: SQL is the lead skill for Data Analysts (60% of postings) while Python is the lead skill for Data Scientists (64%).

Q. Which skills are exclusive to Data Scientist vs Data Analyst?

Data Scientist postings own the modeling stack: Generative AI (14%), LLMs (14%), Apache Spark (13%), TensorFlow (13%), PyTorch (13%), scikit-learn (11%), Deep Learning (10%), and NLP (10%) all clear our exclusivity threshold. Data Analyst postings own the BI and spreadsheet stack: Looker (12%) and Data Modeling (9%) are the only skills with strong exclusivity, but Tableau, Power BI, and Excel each appear in Data Analyst postings 2 to 3 times more often than in Data Scientist postings.

Q. Is it easier to break in as a Data Analyst or a Data Scientist?

Entry-level share is similar: 8% of Data Analyst postings and 9% of Data Scientist postings are tagged entry-level. The bigger contrast is the ceiling: staff-level roles make up 13% of Data Scientist postings versus only 7% for Data Analyst, so the senior career runway is longer in Data Science. Mid-level dominates both (61% Data Analyst, 54% Data Scientist).

Q. Are Data Scientist jobs more remote-friendly than Data Analyst jobs?

No, the two roles are nearly identical on work mode. Onsite is the dominant tag for both (57% in each), hybrid is around one-third (33% Data Analyst, 32% Data Scientist), and fully-remote tags appear on 22% of Data Analyst and 21% of Data Scientist postings. Geography also matches closely: the US is 39% of postings in both, India is 11% to 12%, and the UK is 5%.

Q. Should I become a Data Analyst or a Data Scientist?

Pick Data Analyst if you want to enter the data field faster, work closer to business stakeholders with BI tools, and accept a lower salary band ($95K US median) for an easier on-ramp. Pick Data Scientist if you are willing to invest in machine learning, statistics, and the Python ML stack (PyTorch, TensorFlow, scikit-learn, MLOps) in exchange for a $32K higher median, a longer staff-level career ladder, and exposure to Generative AI and LLM work that increasingly defines the role.

Final Thoughts

The Data Analyst and Data Scientist titles share half a vocabulary and almost the same labor market, but they price out and ladder out differently. Pick the role that matches the work you actually want to do (stakeholder-facing analysis or model-building), then use the skill data above to close the specific gap that separates today's resume from the role you are targeting. We will refresh this comparison quarterly so the trend lines stay current.

Business Operations Manager Skills in 2026: 4,355 Postings

Gnana — Wed, 13 May 2026 03:45:44 +0000

Business Operations Manager Is the Most Fragmented Role We've Analyzed

Most tech roles converge on a recognizable stack. Data Engineer reduces to Python plus SQL plus pipelines. Data Analyst reduces to SQL plus a BI tool. Business Operations Manager refuses to reduce. The role spans revenue operations at a SaaS company, store operations at a fitness chain, healthcare operations at a hospital network, and logistics operations at a freight carrier, and the resulting skill demand spreads across so many distinct profiles that no single skill is required by half of postings.

To put numbers on it, we looked at every active Business Operations Manager posting on the InterviewStack.io job board as of May 2026, 4,355 listings in total, with skills extracted from descriptions and synonyms collapsed (so dashboards and BI reporting count once under "data visualization", Salesforce and CRM count separately because postings often distinguish them).

The headline: the most common skill in the role, Excel, appears in just 26.7% of postings. The next six skills (Monitoring, Automation, Forecasting, Data Visualization, SQL, Salesforce) each show up in 6-17%. Read the data right and you can see the role splitting into two sub-archetypes hiding behind a single title.

Key findings

4,355 active Business Operations Manager postings analyzed across the live job board as of May 2026.

No skill is table stakes: the most common, Excel, appears in 26.7% of postings (1,163 of 4,355). The role has no single skill that more than half of employers require.

The differentiator tier (5-20%) is where the role gets defined: Monitoring (16.6%), Automation (11.7%), Forecasting (11.4%), Data Visualization (10.8%), SQL (8.0%), and Salesforce (5.8%).

Median US base salary is $90,000 (n=1,132), about $38,300 below the comparable Data Engineer median and $2,800 above the Data Analyst median.

Differentiator skills add $25K to $40K to median US base salary: Salesforce ($128,300), Automation ($127,800), Forecasting ($125,000), Tableau ($125,000), and CRM ($125,000) all sit roughly $35K to $38K above the $90,000 baseline.

Monitoring is a salary discount, not a premium: postings that ask for monitoring pay a median of $65,000 (n=242), $25,000 below the baseline, because the keyword concentrates in physical-operations and facility-monitoring roles.

Mid-level dominates at 76.4% (3,328 postings); senior and staff combined are only 13.8%, the flattest seniority pyramid we have measured.

The US is 60.9% of postings, and onsite work is 77.0%, by far the most onsite-heavy role we have analyzed.

What Skills Define a Business Operations Manager in 2026?

Group every individual skill into the higher-level family it belongs to and count how many postings ask for at least one skill in that family. For most tech roles this exercise produces one or two dominant families at 70-90%. For Business Operations Manager, the largest family barely clears 28%, and the top six families crowd between 8% and 28% with no clear winner.

Share of Business Operations Manager postings that ask for at least one skill in each family. A posting that mentions both Tableau and Power BI counts once under "Data Visualization & BI".

The six families that actually shape the role:

Tools & Infrastructure: 28.1% (overwhelmingly Monitoring and Automation as standalone competencies)
Spreadsheets: 27.1% (almost entirely Excel, with a long Google Sheets tail)
Other: 24.2% (Salesforce, CRM, and scalability, the revenue-and-platform side of the role)
Statistics & Experimentation: 15.3% (Forecasting at 11.4%, A/B Testing at 3.1%)
Data Visualization & BI: 14.8% (Data Visualization as a concept at 10.8%, Tableau 4.4%, Power BI 4.3%, Looker 2.3%)
Querying & SQL: 8.1% (almost entirely SQL itself)

Everything below 8% is informational rather than structural: Coding Languages at 3.7%, Machine Learning & AI at 3.2%, Process & Methodology at 2.3%. The Data Engineering Foundations, Modern Data Stack, and Cloud Platforms families combined account for under 3% of postings, which is the opposite of what a Data Engineer or Data Analyst search returns. Read alongside the Data Engineer skills analysis, the Business Operations Manager profile is much closer to a generalist operations role than to a technical analytics role.

The big tell: the top two families (Tools & Infrastructure and Spreadsheets) are at 28% and 27%, and the third (Other, which is the Salesforce-and-CRM bucket) is at 24%. Three families clustered that close together mean the role really is three different roles wearing the same title, and a job hunter targeting this title needs to know which sub-archetype they are actually applying for.

What Are the Three Tiers of Individual Business Operations Manager Skills?

Drill into individual skills and the picture sharpens. The standard tiering (table stakes above 50%, common 20-50%, differentiator 5-20%) puts almost nothing at the top.

Top individual skills in Business Operations Manager postings, by share of listings that mention them. The role has no table-stakes (50%+) skills. Excel sits alone in the common tier at 26.7%; the differentiator band (5-20%) is where the role's actual character emerges.

Table Stakes (50%+ of postings)

Nothing. Not Excel. Not Salesforce. Not Monitoring. No single skill clears the 50% line, which is unusual enough to be the headline finding of this analysis. Hiring managers do not all agree on what a Business Operations Manager is supposed to know, because the underlying job is genuinely different at a SaaS company than at a fitness chain. A candidate who is excellent at Excel passes the filter at roughly one in four postings; the other three out of four are looking for something else entirely.

Common Expectations (20-50% of postings)

Just one entry:

Excel: 26.7% (1,163 postings) (browse Business Operations Manager openings that ask for Excel)

Excel is the closest thing the role has to a baseline. If you read it as "comfortable with spreadsheets, pivot tables, and formula-driven analysis", that's the one consistent expectation. Even so, it is missing from nearly three-quarters of postings, which says less about Excel and more about how loosely the role is defined.

Differentiators (5-20% of postings)

This is the band where the role's actual character lives.

Monitoring: 16.6% (721 postings)
Automation: 11.7% (510)
Forecasting: 11.4% (495)
Data Visualization: 10.8% (472)
SQL: 8.0% (348) (Business Operations Manager + SQL openings)
Salesforce: 5.8% (251) (Business Operations Manager + Salesforce openings)

Monitoring leads this tier by frequency but, as we will see in the salary section, it is also the one skill in the entire dataset that carries a negative premium. The keyword is doing two very different jobs in two different posting populations: in analytical-ops contexts it means observability and reporting, in physical-ops contexts it means watching cameras, sites, and shift schedules. The salary data lets us tell those apart.

Automation, Forecasting, Data Visualization, SQL, and Salesforce are the analytical core of the role. Add CRM (4.9%), Tableau (4.4%), and Power BI (4.3%) just below the cutoff and you have a coherent "revenue-and-analytics ops" profile that pays meaningfully better than the baseline. The next section makes that explicit.

Long Tail (under 5% of postings)

Below the 5% line, individual skill mentions become harder to read as signal:

CRM: 4.9%
Tableau: 4.4%
Power BI: 4.3%
Scalability: 3.2%
A/B Testing: 3.1%
Google Sheets: 2.4%
Looker: 2.3%
Agile: 2.2%

These are not noise; they are the markers of specialized sub-archetypes. A posting that names both Tableau and Power BI (lift 10.8, the highest co-occurrence in our dataset) is almost certainly a "BI-flexible" analytical operations role, regardless of how few of those postings exist overall. A posting that names CRM plus Salesforce (lift 7.95) is a revenue operations role. We come back to those pairs in the skill-stack section.

Which Business Operations Manager Skills Pay More Than the Baseline?

Salary numbers below are restricted to US postings only (where wage-transparency laws produce consistent disclosure) so they're directly comparable. The numbers are base salary: equity, bonuses, RSUs, and sign-on are not disclosed in postings, so total compensation at top employers is meaningfully higher than what we report here, especially at tech and finance employers in this dataset.

The overall median US base salary for Business Operations Manager postings is $90,000 (n=1,132). That is roughly $38,300 below the comparable Data Engineer median ($128,300) and about $2,800 above the Data Analyst median. Two things are pulling the baseline down: the role is more onsite, lower-wage, and physical-operations-heavy than tech analytical roles, and a meaningful share of postings are at fitness, food-service, and retail-operations employers paying near the regional operations-manager floor.

Median US base salary in USD for postings that mention each skill, among US Business Operations Manager postings with structured salary data. Monitoring is the only differentiator-frequency skill that sits below the role baseline.

The salary spread is wide. Differentiator-tier skills sit $25K to $40K above the $90,000 baseline because the role's analytical sub-archetype concentrates at higher-paying employers.

Largest premiums, roughly $35K to $40K above baseline:

Looker: $130,300 (n=30), about $40,300 above baseline. Small sample; treat as suggestive.
Machine Learning: $130,000 (n=25), about $40,000 above. Very small sample; the few postings that name ML are specialized revenue-ops or growth-ops roles.
Salesforce: $128,300 (n=92), about $38,300 above. The most reliable revenue-ops signal in the data.
Automation: $127,800 (n=130), about $37,800 above.
Forecasting: $125,000 (n=162), about $35,000 above. (Business Operations Manager + Forecasting openings)
Tableau: $125,000 (n=70), about $35,000 above.
CRM: $125,000 (n=78), about $35,000 above.

Premiums of roughly $30K to $33K:

Python: $122,500 (n=30), about $32,500 above. Small sample.
Data Visualization: $120,000 (n=151), about $30,000 above.
SQL: $120,000 (n=137), about $30,000 above.
A/B Testing: $120,000 (n=53), about $30,000 above.

Premiums in the $20K to $25K band:

Agile: $115,000 (n=29), about $25,000 above.
Scalability: $110,600 (n=40), about $20,600 above.

Modest premiums, near baseline:

Google Sheets: $100,000 (n=31), about $10,000 above.
Power BI: $98,600 (n=67), about $8,600 above.
Excel: $91,000 (n=319), about $1,000 above. The most common skill is also the lowest-premium one because it concentrates at every employer in the dataset, including the lower-paying ones.

The negative-premium outlier:

Monitoring: $65,000 (n=242), about $25,000 below baseline.

Monitoring is the one skill in the entire dataset where adding it to your resume correlates with a lower US base salary, not a higher one. The likely reason is keyword overlap: "monitoring" in this dataset is doing double duty, capturing both observability and reporting in analytical-ops contexts and physical site, shift, and facility monitoring in operations-floor contexts. The latter cluster pays much less, and the sample size is large enough (n=242) that it drives the median down. Read it as "monitoring on its own is not a salary signal", and pair it with another differentiator before drawing conclusions.

The practical pattern: the role rewards specialization. Excel by itself moves the median by about $1,000. Salesforce, Automation, Forecasting, Tableau, or CRM each move it by $35,000 or more, because each anchors a posting in a sub-archetype that hires from a smaller, better-paid pool. Picking up one revenue-ops tool (Salesforce or CRM) and one analytical tool (SQL, Tableau, or Forecasting) is the most defensible single move a candidate in this role can make.

What Is the Dominant Business Operations Manager Skill Stack?

We computed every two-skill co-occurrence among the top 25 skills to find combinations that appear together more often than chance. Lift greater than 1 means two skills appear together more than their individual frequencies would predict; lift below 1 means they avoid each other.

The most over-represented pairs (highest lift):

Skill pair	Postings	% of postings	Lift
Power BI + Tableau	89	2.0%	10.8
Looker + SQL	72	1.7%	9.1
CRM + Salesforce	98	2.3%	8.0
SQL + Tableau	102	2.3%	6.6
A/B Testing + SQL	59	1.4%	5.6
Data Visualization + Tableau	113	2.6%	5.4
Data Visualization + Power BI	104	2.4%	5.2
Data Visualization + SQL	133	3.1%	3.5
Excel + Google Sheets	85	2.0%	3.1
Excel + Power BI	133	3.1%	2.7

The largest pairs by raw volume tell a different story:

Skill pair	Postings	% of postings	Lift
Data Visualization + Excel	207	4.8%	1.6
Excel + SQL	181	4.2%	2.0
Automation + Excel	167	3.8%	1.2
Excel + Forecasting	163	3.7%	1.2
Automation + Data Visualization	146	3.4%	2.6

Read together, the two tables show two coherent sub-archetypes:

The Excel-first generalist ops stack. Excel pairs with Data Visualization (207 postings), SQL (181), Automation (167), and Forecasting (163) in the largest co-occurrences by volume. The lift on most of these is modest (1.2 to 2.0), which is what you would expect if Excel is the "always-on" skill that other skills get layered onto in a generalist coordinator profile.
The analytical-and-revenue ops stack. Power BI + Tableau (lift 10.8), CRM + Salesforce (lift 8.0), SQL + Tableau (lift 6.6), Looker + SQL (lift 9.1), and A/B Testing + SQL (lift 5.6) all sit at much higher lifts. These pairs are rare overall but, when they appear, they appear together, which is the signature of a specialized sub-population.

The CRM + Salesforce pair (lift 8.0, 98 postings) is the clearest revenue-ops marker in the data. The Power BI + Tableau and SQL + Tableau pairs are the clearest BI-analytical-ops markers. A candidate who can credibly speak to both an analytical tool and a CRM is hitting two of the highest-paying sub-archetypes simultaneously.

Note: co-occurrence statistics here cover only the top 25 skills in the role. Pairs involving smaller-tail skills exist but are not in the dataset above.

Who's Hiring at Which Seniority Level?

We tagged each posting's seniority based on title keywords (Senior, Lead, Principal, Junior, Intern). Postings with no explicit signal default to mid-level.

Seniority distribution of Business Operations Manager postings.

Mid-level: 76.4% (3,328 postings)
Entry: 9.8% (427) (entry-level Business Operations Manager openings)
Senior: 8.7% (379)
Staff / Lead / Principal: 5.1% (221)

The distribution is unusually flat at the top and unusually wide in the middle. Three out of four postings carry no explicit seniority signal at all, defaulting to mid-level, which suggests "Manager" in this title is often a descriptor of scope rather than a step on a people-management ladder. Many Business Operations Manager postings are individual-contributor jobs whose holder coordinates work across teams without managing anyone directly.

Two implications for job hunters:

The entry door is real. Roughly 10% of postings are explicitly entry-level, about three times the share for Data Engineer (3%). For career-switchers from non-technical backgrounds, Business Operations Manager is one of the more open doors into a role that touches data, analytics, and process work.
The senior ladder is thin. Only 13.8% of postings combined are senior or staff. If you are targeting a director-track career, this title is a stepping stone rather than a destination; the named "Director of Operations" and "VP of Operations" roles sit in different listings on the board.

Where Are Business Operations Manager Jobs Located, and How Remote-Friendly Are They?

Geography is far more US-concentrated for this role than for any tech role we have analyzed, and the work mode is far more onsite.

Top countries by share of Business Operations Manager postings.

United States: 60.9% (2,652 postings) (US-only Business Operations Manager openings)
India: 5.1% (222)
United Kingdom: 4.7% (205)
Canada: 4.2% (182)
Philippines: 2.0% (89)
Germany: 1.9% (82)
Mexico: 1.4% (61)
Australia: 1.2% (54)

The US share is the highest we have seen for any role on the board. Where Data Engineer postings split roughly 29% US and 23% India, Business Operations Manager is 61% US and only 5% India, because the work is overwhelmingly physical, in-person, and tied to a specific facility, store, restaurant, clinic, or office. Global capability centers in India do not exist for this role the way they do for software engineering or pipeline work.

The work-mode picture reinforces that geography.

Share of Business Operations Manager postings tagged with each work mode. Some postings carry multiple tags (e.g., "Hybrid or Remote"), so percentages can sum to more than 100%.

Onsite: 77.0% (3,355 postings)
Hybrid: 18.2% (792)
Remote: 12.1% (529) (fully-remote Business Operations Manager openings)

The 77% onsite share is the highest we have seen for any role. For comparison, Data Engineer postings sit at 50% onsite and Data Analyst postings at about 64%. The reason is in the company list (below): a meaningful share of these postings are at fitness chains, healthcare networks, food-service companies, and logistics carriers, all of which need someone physically near the operation. The fully-remote slice is real but small, and it concentrates at SaaS and digital-media employers running revenue-ops or growth-ops teams.

Who's Hiring Business Operations Managers in 2026?

The top hiring companies confirm what the geography and work-mode numbers already suggested. The role is dominated by physical-operations, healthcare, food-service, and consulting employers rather than the tech-first companies that dominate Data Engineer and ML Engineer hiring.

Top companies by active Business Operations Manager postings. Counts include all locations of the same job.

EōS Fitness: 176 postings (fitness operator)
Accenture: 84 (global consulting)
CVS Health: 79 (retail healthcare)
Insomnia Cookies: 40 (food service)
DoorDash: 37 (food delivery, the most tech-native employer near the top)
Dental Depot: 36 (healthcare)
Teleperformance: 33 (business process outsourcing)
Jobgether: 31 (job-aggregator/staffing)
SunOpta: 31 (food manufacturing)
Penske: 28 (logistics)
Dayton Freight: 22 (freight logistics)
Marriott International: 20 (hospitality)

The composition is the inverse of a typical software-engineering top-companies list. Two themes:

Physical operations dominate. Fitness, healthcare, food service, logistics, and hospitality account for the majority of the top 12. These postings are nearly all onsite and concentrated near specific facilities; the role description usually centers on staffing, scheduling, inventory, cost control, and process improvement at a specific site or region.
The analytical sub-archetype hides in the long tail. Companies like DoorDash and the consulting firms run a different version of the role focused on revenue operations, growth operations, or strategy-and-operations. Those listings are individually larger by salary and skill expectations than the median in the dataset, but they are a small share of the volume.

A job hunter using a board filter should expect the default Business Operations Manager query to return a mix of both worlds. Layering a skill filter (+ SQL, + Salesforce, + Tableau) narrows aggressively toward the analytical sub-archetype.

How to Use This in Your Job Search

If you're preparing for a Business Operations Manager job hunt, the data points to a clear sequence.

1. Decide which sub-archetype you are targeting. The role splits cleanly into two profiles: an Excel-first generalist coordinator at fitness, food, healthcare, retail, and logistics employers, and an analytical revenue-or-growth ops profile at SaaS, consulting, and digital-media employers. The first is much easier to enter, the second pays $30K to $40K more, and the skills overlap less than the shared title suggests. Pick one and tune your resume to it before applying.

2. Build Excel deeply, then add a specialist tool. Excel is the one near-universal expectation, but on its own it moves the median by about $1,000. Pairing it with one analytical tool (SQL, Tableau, or Power BI) or one revenue-ops tool (Salesforce, paired with CRM concepts) is the single highest-leverage move a candidate can make. The salary data is unambiguous: those tools each carry roughly a $30K to $38K premium over Excel alone.

3. Drill the topics that actually come up. Reading about Business Operations Manager skills is easy; performing under interview conditions is the hard part. Our interactive courses cover the foundations across SQL, statistics, and business analysis. The question bank lets you drill business analytics, case-style operations questions, and SQL one topic at a time. AI mock interviews put you under realistic interview conditions with on-demand feedback on the case-study and behavioral rounds that dominate this role's loops.

4. Use company-specific prep where it matters. For named consulting and operations-heavy employers, the rounds and expectations vary widely between, say, a fitness operator and a SaaS revenue ops team. Our interview preparation guides break down the rounds, topic priorities, and behavioral expectations company by company so you are not walking into a generic loop.

5. Filter the job board for your stack. Browse current Business Operations Manager openings on the InterviewStack.io job board and combine role and skill filters to narrow to your exact sub-archetype, for example Business Operations Manager + Salesforce for revenue-ops listings, or Business Operations Manager + SQL + Tableau for analytical-ops listings. The board updates daily, so the listings stay current.

FAQ

Q. What skills do companies want for Business Operations Manager roles in 2026?

No single skill is required across the majority of postings, which makes Business Operations Manager one of the most fragmented roles we track. Excel is the most common requirement at 26.7% (1,163 of 4,355 postings). The differentiator tier (5-20% of postings) covers Monitoring (16.6%), Automation (11.7%), Forecasting (11.4%), Data Visualization (10.8%), SQL (8.0%), and Salesforce (5.8%). Tableau, Power BI, CRM, A/B Testing, Google Sheets, Looker, and Agile each appear in 2-5% of postings.

Q. What is the median Business Operations Manager salary in 2026?

The median US base salary across 1,132 Business Operations Manager postings with disclosed pay is $90,000. That figure excludes equity, bonuses, and sign-on, so total compensation at top employers runs higher than what postings list. The baseline is lower than analytical roles (Data Analyst $87,200, Data Engineer $128,300) because the dataset blends true analytical operations work with general operations management at fitness, retail, healthcare, and food-service employers.

Q. Which Business Operations Manager skills pay the highest premium over the role baseline?

Among US postings, the largest premiums attach to revenue-operations and analytical tooling. Salesforce ($128,300, n=92), Automation ($127,800, n=130), Forecasting ($125,000, n=162), Tableau ($125,000, n=70), and CRM ($125,000, n=78) all sit roughly $35K to $38K above the $90,000 baseline. Data Visualization ($120,000), SQL ($120,000), and A/B Testing ($120,000) follow at about $30K above. Looker ($130,300, n=30) leads the table but on a small sample.

Q. Is Business Operations Manager a good entry-level role to break into?

It is more accessible than most tech roles. Roughly 9.8% of postings are tagged entry-level (427 of 4,355), about three times the entry-level share of Data Engineer (3%) and similar to Data Analyst (8%). Mid-level dominates the role at 76.4% (3,328 postings). Senior and staff combined are only 13.8%, which is one of the flattest seniority distributions we have seen and reflects that many Business Operations Manager titles are individual-contributor jobs rather than people-manager tracks.

Q. Where are Business Operations Manager jobs located, and how remote-friendly are they?

The role is heavily concentrated in the United States, with 60.9% of postings (2,652 of 4,355) compared with the high-20s for Data Engineer. India (5.1%), the United Kingdom (4.7%), Canada (4.2%), the Philippines (2.0%), and Germany (1.9%) round out the next markets. Work mode is sharply onsite: 77.0% of postings are onsite, 18.2% hybrid, and only 12.1% remote, by far the lowest remote share of any role we have analyzed.

Q. Which companies hire the most Business Operations Managers in 2026?

The top hiring companies are dominated by physical-operations, healthcare, food-service, and logistics employers rather than tech: EōS Fitness (176 active postings), Accenture (84), CVS Health (79), Insomnia Cookies (40), DoorDash (37), Dental Depot (36), Teleperformance (33), Jobgether (31), SunOpta (31), Penske (28), Dayton Freight (22), and Marriott International (20). Tech-native employers are a smaller share of the top of the list than they are for analytical roles.

Q. What is the dominant Business Operations Manager skill stack in 2026?

There is no single dominant stack. The data points to two coherent sub-archetypes. The Excel-first generalist stack pairs Excel with Data Visualization (207 postings, lift 1.64), SQL (181, lift 1.95), Automation (167, lift 1.23), and Forecasting (163, lift 1.23). The revenue-and-BI stack pairs Salesforce with CRM (98 postings, lift 7.95), Tableau with Power BI (89, lift 10.8), SQL with Tableau (102, lift 6.61), and Looker with SQL (72, lift 9.1). The high lifts on the BI and CRM pairs signal a real, specialized analytical-ops profile inside the broader role.

Final Thoughts

Business Operations Manager is two jobs in one title, and the gap between them is wider than the data on a typical job board suggests. Picking a sub-archetype and building the skills that anchor it (Excel plus SQL plus a BI tool for analytical ops, or Excel plus Salesforce plus CRM for revenue ops) is the most defensible move a candidate can make. The role rewards the candidates who refuse to be generalists about it.

We'll refresh this analysis quarterly so the trend lines stay current.

AI Engineer Skills Companies Want in 2026: 3,449-Posting Analysis

Gnana — Tue, 12 May 2026 00:57:30 +0000

The AI Engineer Title Has Settled Around the LLM Stack

Two years ago, "AI Engineer" was a fuzzy keyword that could mean almost anything: an ML researcher, a data scientist with a Python script, a backend engineer who fine-tuned a model once. In 2026 it has settled into a much more specific job: take a foundation model, wrap it in retrieval, monitoring, and an API, and ship it into a product. The variance lives in which model provider, which vector store, and which orchestration framework, not in what the work is.

To put numbers on it, we looked at every active AI Engineer posting on the InterviewStack.io job board as of May 2026, 3,449 listings, with skills extracted from descriptions and synonyms collapsed (so gen ai and generative ai count once, gcp and google cloud count once).

The headline: an AI Engineer posting in 2026 is, on average, a Python job plus an LLM job plus a retrieval job plus a cloud job rolled into one. Two skills appear in roughly two-thirds of postings or more, the RAG-plus-LangChain pattern has crossed the common-tier line, and a quiet salary premium has attached itself to anyone who can also handle the distributed-systems work behind those applications.

Key findings

3,449 active AI Engineer postings analyzed across the live job board as of May 2026.

Python (71%) and LLMs (66%) are the only two table-stakes skills; 1,821 postings (53%) ask for both together.

The LLM application stack has moved from differentiator to common: RAG (40%), Generative AI (39%), LangChain (25%), and OpenAI (20%) all now sit in the 20-50% common tier.

Median US base salary is $146,000 (n=636), one of the highest role medians on our board.

Distributed-systems and data-platform skills carry the biggest salary premiums: Distributed Systems ($180K, +$34K), Kafka ($171,500, +$25.5K), Apache Spark ($170K, +$24K), and Snowflake ($170K, +$24K).

Only 6% of postings are entry-level (206 of 3,449); senior plus staff roles together make up 40% of the market.

The US is 36% of postings, India is 13%: a much US-heavier mix than the Data Engineer market, where India is 23%.

Onsite is still the default at 50% of postings; 34% are hybrid and 27% are remote (postings can carry multiple tags).

What Skill Families Define an AI Engineer Role in 2026?

Group every individual skill into the higher-level family it belongs to and count how many postings ask for at least one skill in that family. The role's actual shape emerges as a stack, not a single specialty, with the LLM application layer sitting on top of a software-engineering and cloud foundation.

Share of AI Engineer postings that ask for at least one skill in each family. A posting that mentions both PyTorch and TensorFlow counts once under "Machine Learning & AI".

The families that actually define the role:

Machine Learning & AI: 87% (LLMs, generative AI, machine learning, PyTorch, MLOps, TensorFlow, NLP, deep learning, computer vision, scikit-learn, pandas)
LLM Application Stack: 86% (RAG, LangChain, OpenAI, APIs, observability, vector databases, embeddings, Bedrock, FastAPI, scalability, microservices, distributed systems)
Coding Languages: 75% (overwhelmingly Python, with TypeScript, Java, and JavaScript as secondary languages)
Tools & Infrastructure: 67% (automation, monitoring, Docker, Kubernetes, Git, GitHub)
Cloud Platforms: 47% (AWS, Azure, Google Cloud)
Statistics & Experimentation: 37% (A/B testing, statistics)
Data Engineering Foundations: 29% (data pipelines, Apache Spark)
Querying & SQL: 24% (almost entirely SQL itself)

The "LLM Application Stack" family is what makes this role distinct. It bundles the skills you only see clustered together on postings that are productionizing LLMs: retrieval-augmented generation, vector stores, embeddings, the model-provider SDKs (OpenAI, Bedrock), and the operational skills (observability, scalability, distributed systems) needed to run them at scale. Two years ago this cluster did not exist as a coherent stack; today it appears in roughly the same share of postings as the underlying ML family.

The smallest families are also informative. Modern Data Stack sits at 15% and Data Visualization & BI at 15%, the inverse of what a Data Engineer posting looks like. The AI Engineer is rarely expected to build dashboards or own the warehouse; they consume the data once it lands. Read alongside the Data Engineer skills analysis, the contrast is sharp: the engineer stack centers on pipelines, warehouses, and orchestration; the AI Engineer stack centers on models, retrieval, and inference APIs.

What Are the Three Tiers of Individual AI Engineer Skills?

Drill into individual skills inside those families and three tiers emerge.

Top individual skills in AI Engineer postings, by share of listings that mention them. Skills above 50% are table stakes; 20-50% are common; 5-20% are differentiators. The data normalizer splits "llm" and "llms" into two buckets, which is why the chart shows both; in practice they refer to the same concept.

Table Stakes (50%+ of postings)

These appear in more than half of all AI Engineer postings. If your resume cannot credibly demonstrate them, you are filtered out before a recruiter reads a line.

Python: 71% (AI Engineer + Python openings)
LLMs: 66% (AI Engineer + LLM openings)

The table-stakes set is unusually narrow: just Python and LLMs. There is essentially no AI Engineer job in 2026 that does not involve writing Python that calls or fine-tunes a large language model. The two appear together in 1,821 postings, or 53% of the entire market, the single most common skill pair in the dataset and the closest thing to a canonical AI Engineer stack.

Worth noting: classical ML tooling like scikit-learn (8%) and even PyTorch (23%, common-tier) sit well below the table-stakes line. The role is no longer a research-ML role; it is an application role where the model is mostly a given and the engineering is around it.

Common Expectations (20-50% of postings)

This is where the LLM application stack lives.

RAG (Retrieval-Augmented Generation, fetching context from a vector store and feeding it to an LLM): 40% (AI Engineer + RAG openings)
Generative AI: 39%
Machine Learning: 38%
AWS: 37% (AI Engineer + AWS openings)
Automation: 34%
APIs: 33%
Azure: 33%
Monitoring: 31%
Google Cloud: 25%
LangChain (an open-source framework for chaining LLM calls, prompts, and tools): 25% (AI Engineer + LangChain openings)
CI/CD: 24%
A/B Testing: 24%
PyTorch: 23%
OpenAI: 20%

This tier is dominated by the LLM application stack. RAG (40%), LangChain (25%), and the OpenAI SDK (20%) all sit firmly in common-tier territory, a transition that happened over the last 18 months. As recently as 2024, RAG and LangChain were resume differentiators; they are now baseline expectations on a serious AI Engineer posting.

The cloud picture is similar to the rest of tech: AWS leads at 37%, Azure at 33%, Google Cloud at 25%. A candidate fluent in any one of the three is in the running for most postings; a candidate fluent in zero of them is struggling on roughly half the market, since most AI Engineer roles ship to production on a managed-cloud LLM service or hosted inference endpoint.

The A/B Testing entry (24%) is the most surprising line in the common tier. AI Engineers are increasingly responsible for measuring the impact of the systems they build, not just shipping them, so postings now bundle experimentation into the job description.

Differentiators (5-20% of postings)

These show up in a minority of postings but signal a more specialized, and, as we will see, often better-paid role.

SQL: 19%
Observability: 19%
Vector Databases: 18%
MLOps: 18%
Data Pipelines: 18%
Docker: 18%
TensorFlow: 17%
Kubernetes: 16%
NLP: 15%
Scalability: 15%
Data Visualization: 13%
TypeScript: 13%
Embeddings: 12%
Deep Learning: 12%
Agile: 12%
Statistics: 12%
Java: 10%
Git: 9%
Prototyping: 9%
Containerization: 8%
JavaScript: 8%
scikit-learn: 8%
Microservices: 8%
FastAPI (a Python framework for building production APIs): 8%
Databricks: 7%
React: 7%
Computer Vision: 7%
Distributed Systems: 7%
Bedrock (AWS's hosted-LLM service): 7%
GitHub: 7%
Apache Spark: 6%
pandas: 6%
System Design: 6%

The infrastructure tier (Kubernetes, Docker, distributed systems, observability, MLOps) sits between 7% and 19%. None of them are required for most AI Engineer roles, but they are the skills that separate "AI Engineer who can run a demo" from "AI Engineer who can ship a production LLM application that stays up and gets debugged when it does not."

The vector-databases line (18%) is the cleanest signal in the data that the role has gotten serious about retrieval. Two years ago, almost no posting named a vector store; today, nearly one in five does, and the cluster of vector databases plus embeddings plus RAG appears almost exclusively together. If you are early in your career, learning one vector store deeply (most teams pick pgvector, Pinecone, Weaviate, or Qdrant) is a high-leverage move.

Which AI Engineer Skills Pay More Than the Baseline?

The overall median US base salary for AI Engineer postings is $146,000 (n=636). That is roughly $17,700 above the comparable median for Data Engineer postings ($128,300) and about $58,800 above the Data Analyst median ($87,200), a real, structural premium for the role's higher applied-AI and systems bar.

Median US base salary in USD for postings that mention each skill, among US AI Engineer postings with structured salary data.

The top-paying skills cluster around distributed systems and the data-platform layer, not the LLM application skills themselves. Skills with the largest premiums above the $146,000 baseline:

Distributed Systems: $180,000 (n=41), about $34,000 above baseline
Kafka: $171,500 (n=27), about $25,500 above baseline
Apache Spark: $170,000 (n=53), about $24,000 above baseline
Snowflake: $170,000 (n=51), about $24,000 above baseline

A second cluster of platform and applied-AI skills sits at roughly $150,000, premiums in the $4K to $7K range above baseline:

React: $152,800 (n=41), about $6,800 above baseline
A/B Testing: $152,400 (n=166), about $6,400 above baseline
Observability: $151,800 (n=117), about $5,800 above baseline
MLOps: $150,300 (n=88), about $4,300 above baseline
LLMs: $150,000 (n=421), about $4,000 above baseline
Computer Vision: $150,000 (n=41), about $4,000 above baseline
Monitoring: $150,000 (n=192), about $4,000 above baseline
Embeddings: $150,000 (n=87), about $4,000 above baseline
Scalability: $150,000 (n=82), about $4,000 above baseline
Microservices: $150,000 (n=41), about $4,000 above baseline
System Design: $150,000 (n=31), about $4,000 above baseline

Skills that sit right at or slightly below baseline include LangChain ($145,000, n=139), PyTorch ($145,000, n=139), TensorFlow ($145,000, n=100), AWS ($145,000, n=245), Google Cloud ($145,000, n=150), Databricks ($145,000, n=56), and RAG ($147,100, n=245). The foundation skills (Python at $143,000, n=415; Generative AI at $140,000, n=250; Machine Learning at $140,000, n=249) actually sit a few thousand below baseline, the classic "every posting asks for this, so it does not differentiate" pattern.

The pattern is clear. The LLM application skills (RAG, LangChain, OpenAI) are now so common that they no longer carry a salary premium; they are required, not differentiating. The premium has shifted to the layer beneath: the engineers who can also handle distributed systems, streaming (Kafka), large-scale compute (Spark), and the data platform (Snowflake) are the ones who get paid for the combination. That is the practical signal in the data: an AI Engineer who can also do the heavy data-platform work earns roughly $20K to $30K more than one who only does the application layer.

Our interview-prep courses cover the foundations across system design, distributed systems, and ML; the question bank is where you drill the topics that come up in onsite rounds for the higher-premium specialties.

What Is the Dominant AI Engineer Skill Stack?

We computed every two-skill co-occurrence among the top 25 skills to find the combinations that show up together more often than chance.

The strongest pairs by lift, where lift greater than 1 means the two skills appear together more often than their individual frequencies would predict:

Skill pair	Postings that mention both	% of postings	Lift
LLMs + RAG	1,228	36%	1.34
LangChain + LLMs	768	22%	1.33
Generative AI + RAG	718	21%	1.33
AWS + RAG	711	21%	1.38
Python + PyTorch	731	21%	1.28
LangChain + Python	781	23%	1.26
APIs + LLMs	921	27%	1.23
CI/CD + Python	710	21%	1.21
Google Cloud + Python	744	22%	1.20
AWS + Python	1,082	31%	1.18
Python + LLMs	1,821	53%	1.12

Each pair tells you something concrete about how postings actually compose skills:

LLMs + RAG (lift 1.34) is the strongest "what is the work" pair in the dataset. Postings that mention LLMs are 34% more likely to also mention RAG than baseline, because the dominant AI Engineer pattern in 2026 is not "fine-tune your own model" but "retrieve the right context and feed it to a foundation model".
LangChain + LLMs (lift 1.33) and LangChain + Python (lift 1.26) signal how the work is built. Teams that adopt LangChain are looking for engineers who can chain LLM calls, tools, and retrieval steps in Python, not just write prompts.
AWS + RAG (lift 1.38) is the highest-lift cloud pair. Companies on AWS are disproportionately the ones running production RAG systems, almost certainly because of Bedrock plus OpenSearch plus S3 plus Lambda forming a managed stack for it.
Python + PyTorch (lift 1.28) marks the postings that still ask for model-level work alongside the application layer: fine-tuning, custom embedding models, or production inference in PyTorch rather than via a hosted API.
APIs + LLMs (lift 1.23) and CI/CD + Python (lift 1.21) describe the productionization layer: postings that mention LLMs are 23% more likely to also ask for API design, and pipeline-as-code discipline is bundled with the role at almost the same rate.
Python + LLMs (lift 1.12) is the dominant base stack. With 1,821 postings asking for both, Python + LLM AI Engineer roles make up 53% of the entire market, the closest thing to a single canonical AI Engineer stack.

The pattern: companies want a base layer (Python plus LLMs), a retrieval layer (RAG plus a vector store), an orchestration layer (LangChain or equivalent), a productionization layer (APIs, CI/CD, monitoring), and a cloud (AWS, Azure, or GCP). The "prompt engineer" role that some 2023 postings tried to describe does not exist in AI Engineer hiring; the role is a full-stack production engineer who happens to specialize in LLM applications.

Who's Hiring at Which Seniority Level?

We tagged each posting's seniority based on title keywords (Senior, Lead, Principal, Junior, Intern). Postings with no explicit signal default to mid-level.

Seniority distribution of AI Engineer postings.

Mid-level: 54% (1,874 postings)
Senior: 22% (747) (senior AI Engineer openings)
Staff / Lead / Principal: 18% (622)
Entry: 6% (206)

Two things stand out. First, only 6% of postings are explicitly entry-level, a narrower door than Data Analyst hiring (8% entry-level) but a wider one than Data Engineer hiring (3% entry-level). The bar for AI Engineer entry is higher than it sounds in the AI hype cycle, because most companies expect candidates to have shipped at least one applied-LLM or ML project somewhere first. Backend engineers, ML engineers, and data engineers transitioning in have an easier time than career-switchers from non-coding roles.

Second, the senior-and-above tiers (senior plus staff) are 40% of all postings. There is real career runway on the IC track, with substantial demand for staff-level engineers who can architect retrieval systems and inference platforms rather than just glue LLMs together. If you are targeting senior or staff AI Engineer roles, expect the differentiator skills (distributed systems, MLOps, observability, Kafka, Spark) to be required, not optional.

Where Are AI Engineer Jobs Located, and How Remote-Friendly Are They?

Geography for AI Engineer roles is much more US-concentrated than Data Engineer hiring, where India makes up nearly a quarter of postings. AI Engineer hiring is still flowing primarily through US tech and consulting.

Top countries by share of AI Engineer postings.

United States: 36% (US-only AI Engineer openings)
India: 13%
United Kingdom: 5%
Canada: 5%
Germany: 4%
Singapore: 3%
France: 2%
Spain: 2%
Poland: 2%
Netherlands: 2%

The US is the dominant single market at more than a third of all postings. India is a distant second at 13%, roughly half its share of the Data Engineer market, a reflection of how much AI Engineer demand still concentrates in US-based product companies, AI labs, and the major consultancies' US delivery practices. The Singapore line (3%) is notable; it punches above its weight relative to most other tech roles, driven by a cluster of AI hiring at regional tech firms and a research university (Nanyang Technological University) that shows up in the top employers below.

The "AI Engineer is a perfect remote-first role" assumption is partly true, but onsite still leads.

Share of AI Engineer postings tagged with each work mode. Some postings carry multiple tags (e.g., "Hybrid or Remote"), so percentages sum to more than 100%.

Onsite: 50% of postings (1,735)
Hybrid: 34% (1,157)
Remote: 27% (926) (fully-remote AI Engineer openings)

Postings can carry multiple work-mode tags when a company says "Hybrid or Remote", which is why the percentages sum to more than 100%. Fully remote AI Engineer roles do exist and are comparable in share to Data Engineer roles (27% in both), but the dominant mode is still onsite. The remote share concentrates in AI-native startups and product-led tech companies; financial services, consulting, and government default to onsite or hybrid.

Who's Hiring AI Engineers in 2026?

The top hiring companies on our board mix Big Four consulting, AI-native product companies, frontier-model labs, enterprise software, and a meaningful staffing-and-aggregator tail.

Top companies by active AI Engineer postings. Counts include all locations of the same job.

PricewaterhouseCoopers: 97 postings (Big Four consulting)
Hyphen Connect Limited: 45 (staffing and recruiting)
EverAI: 38 (AI product company)
Jobgether: 35 (job aggregator and staffing)
NVIDIA: 32 (AI hardware and platforms)
Nanyang Technological University: 27 (academic research)
Celonis SE: 27 (process-mining enterprise software)
Sezzle: 26 (fintech)
Huawei Technologies Canada: 24 (telecom research)
Exadel: 21 (software services)
Accenture: 21 (global consulting)
Micron Technology: 20 (semiconductors)

A few names worth flagging further down the top 20: Mistral AI (16 postings) is the only frontier-model lab in the list, AstraZeneca (16) and Royal Bank of Canada (15) represent the pharma and banking pull into AI Engineering, and Nebius Academy (17) shows the training and education segment building out its own AI Engineering teams.

A few of the highest-volume entries (Hyphen Connect, Jobgether, Jack & Jill, Nexthire) are staffing and aggregator brands that re-post roles for many client companies, which is why their counts run high; the direct-employer leaders on the list are PwC, NVIDIA, EverAI, Celonis, Sezzle, Huawei Canada, Accenture, Micron, Mistral AI, AstraZeneca, and RBC.

The shape of the list confirms two things the rest of the data already suggested. First, a meaningful share of AI Engineer demand still flows through consulting firms, not direct posts from end employers; PwC, Accenture, Capco, and Booz Allen Hamilton together account for more than 150 listings. Second, the rest is genuinely diverse: AI-native product companies (EverAI, Mistral AI), AI infrastructure (NVIDIA, Micron), enterprise software (Celonis), fintech (Sezzle, RBC), pharma (AstraZeneca), and academia (Nanyang). There is no single industry that dominates AI Engineer hiring, which is unusual for a fast-growing role. For specific company processes, our interview preparation guides break down the rounds, topic priorities, and behavioral expectations company by company.

How to Use This in Your Job Search

If you are preparing for an AI Engineer job hunt, the data points to a clear sequence.

1. Build the two table-stakes skills ruthlessly. Python and LLM application work are the two filters every posting applies. Not weekend-tutorial Python, production Python: writing testable modules, handling errors, packaging code that runs reliably behind an API. Not "I ran a ChatGPT prompt", LLM application work: building a real retrieval system, evaluating outputs, handling latency, debugging hallucinations in production. The two appear together in 53% of postings, the single most common pair in the dataset.

2. Pick a cloud, a vector store, and a framework. AWS is the largest single cloud at 37%, with the strongest tie to RAG (lift 1.38), but Azure (33%) and Google Cloud (25%) cover comparable ground in their respective company segments. For retrieval, pick one vector store and learn it deeply (pgvector, Pinecone, Weaviate, or Qdrant are the common picks); the postings cluster vector databases (18%) and embeddings (12%) together, so the two skills are bundled. For orchestration, LangChain is the safest default at 25% of postings, but understanding the underlying pattern (chains, tools, retrievers) matters more than the specific library.

3. Add a differentiator from the platform layer. The salary data is unambiguous: the skills companies pay the largest premiums for are not the LLM application skills themselves but the platform layer beneath them. Distributed Systems, Kafka, Apache Spark, and Snowflake each move your median US base salary by $24K to $34K over the role baseline. Pick one that fits the kind of system you want to build (high-throughput streaming, large-scale compute, or warehouse-native data) and learn it deeply enough to talk through trade-offs in an onsite.

4. Drill the topics, then practice the rounds. Reading about AI Engineer skills is easy; performing under interview conditions is the hard part. Our interactive courses cover the foundations across system design, statistics, and applied ML. The question bank lets you drill ML, system design, distributed systems, and LLM application topics one at a time. AI mock interviews let you practice the full round under realistic conditions, with on-demand feedback on system-design and ML-design questions specifically.

5. Filter the job board for your stack. Browse current AI Engineer openings on the InterviewStack.io job board and combine role and skill filters to narrow to your exact stack, e.g., AI Engineer + RAG + AWS or AI Engineer + LangChain + Python. The board updates daily, so the listings are current.

FAQ

Q. What skills do companies want for AI Engineer roles in 2026?

Python and LLMs are table stakes, appearing in 71% and 66% of postings respectively. Above that base, RAG (40%), Generative AI (39%), Machine Learning (38%), AWS (37%), automation (34%), APIs (33%), Azure (33%), monitoring (31%), Google Cloud (25%), LangChain (25%), CI/CD (24%), A/B Testing (24%), PyTorch (23%), and OpenAI (20%) sit in the common tier. Vector databases, MLOps, observability, and distributed systems are differentiator skills that pay real premiums.

Q. What is the median salary for an AI Engineer in 2026?

The median US base salary across 636 AI Engineer postings with disclosed salary data is $146,000. That figure excludes equity, bonuses, and sign-on, so total compensation at top employers runs meaningfully higher, especially at AI-native labs and frontier-model companies.

Q. Which AI Engineer skills pay the highest premium over the role baseline?

Among US postings, the largest premiums attach to distributed-systems and data-platform specialties. Distributed Systems ($180,000, +$34K over the $146,000 baseline), Kafka ($171,500, +$25.5K), Apache Spark ($170,000, +$24K), and Snowflake ($170,000, +$24K) lead the table. A cluster of platform and applied-AI skills follows at roughly $150,000 (+$4K to +$7K): React, A/B Testing, Observability, MLOps, LLMs, Computer Vision, Monitoring, Embeddings, Scalability, Microservices, and System Design.

Q. Is AI Engineer a good entry-level role to break into?

Entry-level access is narrow but not closed. Only 6% of AI Engineer postings are explicitly entry-level (206 of 3,449), compared with 8% for Data Analyst and 3% for Data Engineer. Most companies expect candidates to have already shipped at least one applied-LLM or ML project, so career switchers typically route through ML engineering, backend, or data-engineering roles before stepping in.

Q. Where are most AI Engineer jobs located, and how remote-friendly are they?

The United States is the largest single market at 36% of postings, followed by India at 13%, the UK (5%), Canada (5%), Germany (4%), and Singapore (3%). About 27% of postings are tagged remote, 34% hybrid, and 50% onsite (some postings carry multiple tags), so onsite remains the dominant default.

Q. Which companies hire the most AI Engineers in 2026?

The top of the list mixes Big Four consulting, AI-native companies, and enterprise software: PricewaterhouseCoopers (97 active postings), Hyphen Connect Limited (45), EverAI (38), Jobgether (35), NVIDIA (32), Celonis SE (27), Nanyang Technological University (27), Sezzle (26), Huawei Technologies Canada (24), Exadel (21), Accenture (21), and Micron Technology (20). Mistral AI, AstraZeneca, and Royal Bank of Canada also appear in the top 20.

Q. What is the dominant AI Engineer skill stack in 2026?

Python plus LLMs is the foundation, appearing together in 1,821 postings (53% of the market) with a co-occurrence lift of 1.12. The most over-represented combinations layer RAG, LangChain, and PyTorch on top of that base: LangChain + LLMs (lift 1.33), Python + PyTorch (1.28), LangChain + Python (1.26), APIs + LLMs (1.23), and CI/CD + Python (1.21) all describe stacks built around productionizing LLM applications.

Final Thoughts

The AI Engineer role in 2026 has settled into a coherent stack (Python plus LLMs plus retrieval plus a cloud) with a real ladder above it and one of the highest role medians on our board. The trade-off is that the application-layer skills (RAG, LangChain, OpenAI) are now so common that they no longer differentiate; the salary premium has migrated to the platform layer underneath. If you can route through a backend, ML, or data-engineering role to build the production and distributed-systems reps, the senior and staff tier opens up quickly, and the differentiator skills compound from there.

We will refresh this analysis quarterly so the trend lines stay current.

Data Engineer Skills Companies Want in 2026: 6,877-Posting Analysis

Gnana — Sat, 09 May 2026 17:09:00 +0000

The Data Engineer Title Has Settled Into a Stack

Where "Data Analyst" still hides three or four very different jobs under one keyword, "Data Engineer" in 2026 is a much more consistent role: build pipelines, model the warehouse, run them on a cloud, keep them observable. The variance lives in which warehouse, which orchestrator, and which cloud, not in what the work is.

To put numbers on it, we looked at every active Data Engineer posting on the InterviewStack.io job board as of May 2026, 6,877 listings, with skills extracted from descriptions and synonyms collapsed (so etl and data pipelines count once, gcp and google cloud count once).

The headline: a Data Engineer posting in 2026 is, on average, a Python job plus a SQL job plus a pipeline-building job plus a cloud job rolled into one. Three skills appear in roughly seven out of every ten postings, and the modern data stack has moved firmly from differentiator to default.

Key findings

6,877 active Data Engineer postings analyzed across the live job board as of May 2026.

Three table-stakes skills cluster near 71-74%: Data Pipelines (74%), SQL (71%), and Python (71%). Python and SQL appear together in 58% of postings (4,002 of 6,877).

The modern data stack is now common, not differentiating: Snowflake (31%), Databricks (29%), Airflow (29%), and dbt (24%) all sit in the 20-50% common tier.

Median US base salary is $128,300 (n=1,183), about $41,100 above the comparable Data Analyst median of $87,200.

Differentiator skills add $8K to $22K to the median US base salary: Distributed Systems, Apache Spark, Observability, dbt, BigQuery, Airflow, and Kafka all sit above the $128,300 baseline.

Only 3% of postings are entry-level (219 of 6,877); senior + staff roles together make up 45% of the market.

The US is 29% of postings, India is 23%: the closest second of any tech role we have analyzed.

Onsite is still the default at 50% of postings; 32% are hybrid and 27% are remote (postings can carry multiple tags).

What Skill Families Define a Data Engineer Role in 2026?

Group every individual skill into the higher-level family it belongs to and count how many postings ask for at least one skill in that family. The role's actual shape emerges as a stack, not a single specialty, but a layered set of competencies a hiring manager expects to see on the same resume.

Share of Data Engineer postings that ask for at least one skill in each family. A posting that mentions both Snowflake and Databricks counts once under "Modern Data Stack".

The families that actually define the role:

Data Engineering Foundations: 89% (data pipelines, data quality, data modeling, warehousing, Spark, Kafka, data lakes)
Querying & SQL: 74% (almost entirely SQL itself, with a long tail of PostgreSQL and NoSQL)
Coding Languages: 74% (overwhelmingly Python, with Scala and Java as secondary languages)
Modern Data Stack: 66% (Snowflake, Databricks, Airflow, dbt, BigQuery, Redshift)
Cloud Platforms: 63% (AWS, Azure, Google Cloud)
Tools & Infrastructure: 63% (monitoring, automation, Git, Docker, Terraform, Kubernetes)
Data Visualization & BI: 40% (mostly the requirement to surface results to stakeholder dashboards)
Machine Learning & AI: 35% (often asking the engineer to support ML pipelines, not build models)

The smallest families are also informative. Statistics & Experimentation sits at 17% and Spreadsheets at 4%, which is the inverse of what a Data Analyst posting looks like. The Data Engineer is rarely expected to run an experiment or live in Excel. Read alongside the Data Analyst skills analysis, the contrast is stark: the analyst stack centers on BI tools and SQL, while the engineer stack centers on pipelines, code, and cloud.

What Are the Three Tiers of Individual Data Engineer Skills?

Drill into individual skills inside those families and three tiers emerge.

Top individual skills in Data Engineer postings, by share of listings that mention them. Skills above 50% are table stakes; 20-50% are common; 5-20% are differentiators. Generic role-keywords like "data engineering" and universal soft skills are filtered out before counting.

Table Stakes (50%+ of postings)

These appear in more than half of all Data Engineer postings. If your resume can't credibly demonstrate them, you're filtered out before a recruiter reads a line.

Data Pipelines: 74%
SQL: 71% (browse Data Engineer openings that ask for SQL)
Python: 71% (Data Engineer + Python openings)

The three table-stakes skills are unusually concentrated. Python and SQL are nearly tied at 71%, and pipeline-building (ETL, ELT, and data integration, all collapsed under "data pipelines") shows up in 74% of postings. There is essentially no Data Engineer job in 2026 that does not involve writing Python that loads SQL-queryable data through a pipeline. A candidate who is strong in two of those three but missing the third is filtering themselves out of three-quarters of the market.

Worth noting: nothing in the BI tool world hits the table-stakes line. Tableau and Power BI sit in the differentiator tier at 11% and 14%. Companies hiring Data Engineers expect them to enable dashboards, not build them.

Common Expectations (20-50% of postings)

This is where the role's character gets defined.

AWS: 44% (Data Engineer + AWS openings)
Data Quality: 43%
Data Modeling: 38%
Data Visualization as a generic skill: 34%
Azure: 33%
Apache Spark: 33% (Data Engineer + Spark openings)
Snowflake: 31% (Data Engineer + Snowflake openings)
Monitoring: 31%
CI/CD: 30%
Databricks: 29%
Airflow: 29% (the open-source orchestrator most data teams use to schedule pipelines)
Data Warehouse as a concept: 27%
Automation: 26%
Google Cloud: 25%
dbt: 24% (a SQL transformation framework that runs inside the data warehouse)
Data Governance: 24%
Scalability: 21%

The tier is dominated by the modern data stack. Snowflake (31%), Databricks (29%), Airflow (29%), and dbt (24%) all sit comfortably above the 20% common-tier line, a transition that happened over the last 24 months. Two years ago, Snowflake and dbt were resume differentiators. They're now common-tier expectations, with the differentiator role shifting to the next layer down (Kafka, BigQuery, Redshift, Delta Lake, PySpark).

The cloud picture is also clear. AWS leads at 44%, Azure at 33%, and Google Cloud at 25%. A candidate fluent in any one of the three is in the running for most postings, but a candidate fluent in zero of them is struggling: about 63% of postings name a specific cloud, and the rest implicitly assume one.

The "Data Quality" and "Data Governance" entries (43% and 24%) deserve attention. Hiring managers are no longer assuming pipelines just work; they're explicitly asking for engineers who instrument them, test them, and govern access to them.

Differentiators (5-20% of postings)

These show up in a minority of postings but signal a more modern, more specialized, and, as we'll see, better-paid role.

Machine Learning: 19%
Kafka: 17%
Data Lake: 15%
BigQuery: 14% (Data Engineer + BigQuery openings)
Power BI: 14%
Observability: 14%
Docker: 14%
Terraform: 14%
S3: 13%
PySpark: 13%
Kubernetes: 13%
Redshift: 12%
Scala: 12%
Java: 12%
Tableau: 11%
LLMs: 9%
Generative AI: 9%
Distributed Systems: 9%
Looker: 9%
Statistics: 7%
Delta Lake: 7%

The streaming and infrastructure tier (Kafka, Kubernetes, Terraform, Docker, observability, distributed systems) sits between 9% and 17%. None of them are required for most Data Engineer roles, but they're the skills that separate a "data engineer who can run a daily ETL job" from a "data engineer who can stand up a real-time streaming platform with infra-as-code."

The Generative AI and LLM line items (each at 9%) are the newest entrants. A year ago they were near zero. They're showing up specifically in postings where the engineer is being asked to build retrieval pipelines, vector stores, or embedding-generation jobs to support an internal AI product.

Which Data Engineer Skills Pay More Than the Baseline?

Salary numbers below are restricted to US postings only (where wage-transparency laws produce consistent disclosure) so they're directly comparable. The numbers are base salary: equity, bonuses, RSUs, and sign-on are not disclosed in postings, so total compensation at top employers is meaningfully higher than what we report here, especially in tech and finance.

The overall median US base salary for Data Engineer postings is $128,300 (n=1,183). That's roughly $41,100 higher than the comparable median for Data Analyst postings ($87,200), a real, structural premium for the role's higher coding and infrastructure bar.

Median US base salary in USD for postings that mention each skill, among US Data Engineer postings with structured salary data.

The top-paying skills cluster around streaming, infrastructure, and modern data stack specialties, not the table stakes. Skills with premiums of roughly $20K to $22K above the $128,300 baseline:

S3, Looker, Docker, Distributed Systems, Data Lake, A/B Testing: all $150,000 (n ranges from 119 to 196), about $21,700 above baseline
Apache Spark: $148,100 (n=416), about $19,800 above baseline
Observability: $148,000 (n=209), about $19,700 above baseline
Monitoring: $147,100 (n=416), about $18,800 above baseline

Skills with premiums of roughly $11K to $12K:

dbt: $140,000 (n=287), about $11,700 above baseline
Scala: $140,000 (n=118), about $11,700 above baseline
BigQuery: $140,000 (n=143), about $11,700 above baseline
Airflow: $139,000 (n=292), about $10,700 above baseline

Smaller premiums (around $7K to $8K):

Kafka: $136,200 (n=216), about $7,900 above baseline
Snowflake: $135,000 (n=395), about $6,700 above baseline

Skills closer to baseline (table-stakes territory):

Data Pipelines: $130,000 (n=912), about $1,700 above baseline
Python: $130,000 (n=895), about $1,700 above baseline
SQL: $128,800 (n=877), about $500 above baseline

Two outliers worth flagging at the top: Dagster clears $153,000 (n=72), a Python-first orchestrator increasingly chosen over Airflow on greenfield platforms, and Sagemaker sits at $160,000 (n=26). Both have smaller samples than the rest of the table, so treat them as suggestive rather than definitive.

The pattern is clear. Skills that show up in nearly every posting have flatter salary distributions because they're a baseline; they don't differentiate one candidate from another. The skills that show up in the minority of postings are the ones companies are willing to pay for, because they're the ones companies struggle to find. Picking up Spark, dbt, Kafka, Airflow, or an observability/distributed-systems specialty raises your median offer by roughly $8K to $22K over the role baseline.

The practical takeaway: the table-stakes skills (Python, SQL, and pipeline-building) get your resume past the filter. The differentiator skills move you up the offer ladder. Build the foundations first, then specialize in streaming (Kafka), modern orchestration (Airflow or Dagster), or distributed compute (Spark plus Databricks) to climb the salary curve. Our interview-prep courses cover the foundations across SQL, Python, and system design; the question bank is where you drill the specific topics that come up in onsite rounds.

What Is the Dominant Data Engineer Skill Stack?

We computed every two-skill co-occurrence among the top 25 skills to find the combinations that show up together more often than chance.

The strongest pairs by lift, where lift greater than 1 means the two skills appear together more often than their individual frequencies would predict:

Skill pair	Postings that mention both	% of postings	Lift
Airflow + Python	1,718	25%	1.22
Airflow + Data Pipelines	1,754	25%	1.20
CI/CD + Python	1,735	25%	1.20
Apache Spark + Python	1,891	27%	1.19
Data Pipelines + Data Quality	2,587	38%	1.18
Data Visualization + SQL	1,963	29%	1.18
Data Modeling + Data Pipelines	2,276	33%	1.17
Snowflake + SQL	1,747	25%	1.16
Python + SQL	4,002	58%	1.15
AWS + Python	2,460	36%	1.15

Each pair tells you something concrete about how postings actually compose skills:

Airflow + Python (lift 1.22) is the strongest pair in the dataset. Postings that mention Airflow are 22% more likely to also mention Python than baseline, because Airflow DAGs are written in Python, and teams adopting it want engineers who can author and debug DAG code, not just operate the scheduler.
CI/CD + Python (lift 1.20) signals the modern pipeline-as-code expectation: postings that ask for CI/CD pipelines also ask for Python, because data engineers are now expected to ship versioned, tested pipelines through the same release process the platform team uses.
Apache Spark + Python (lift 1.19) tells you PySpark is winning. The combination is more common than Spark + Scala, and the salary numbers above show Spark commands a real premium.
Snowflake + SQL (lift 1.16) is the modern warehouse pattern: companies on Snowflake want engineers who can write production SQL inside it, not just point a BI tool at it. The dbt + Snowflake combination is the natural extension and shows up across postings that ask for both.
Python + SQL (lift 1.15) is the dominant base stack. With 4,002 postings asking for both, Python + SQL Data Engineer roles make up 58% of the entire market: the closest thing to a single canonical Data Engineer stack.

The pattern: companies want a base layer (Python plus SQL plus pipeline tooling) plus an orchestrator (Airflow or equivalent), an operations layer (CI/CD, monitoring), and either a warehouse specialty (Snowflake or BigQuery) or a compute specialty (Spark or Databricks). The "SQL plus Excel" world that some Data Analyst postings still inhabit does not exist in Data Engineer hiring.

Who's Hiring at Which Seniority Level?

We tagged each posting's seniority based on title keywords (Senior, Lead, Principal, Junior, Intern). Postings with no explicit signal default to mid-level.

Seniority distribution of Data Engineer postings.

Mid-level: 52% (3,582 postings)
Senior: 31% (2,118) (senior Data Engineer openings)
Staff / Lead / Principal: 14% (958)
Entry: 3% (219)

Two things stand out. First, only 3% of postings are explicitly entry-level, a much harsher pipeline than Data Analyst hiring (8% entry-level) or Software Engineer hiring. Companies overwhelmingly expect Data Engineers to have already built production pipelines somewhere, which makes the role notoriously hard to break into without prior data-adjacent experience. Backend engineers and analytics engineers transitioning in have an easier time than career-switchers from non-coding roles.

Second, the senior-and-above tiers (senior plus staff) are 45% of all postings, one of the most senior-heavy distributions of any tech role. There is real career runway on the IC track, with substantial demand for staff-level engineers who can design platforms rather than just build pipelines. If you're targeting senior or staff Data Engineer roles, expect the differentiator skills (Spark, Kafka, Terraform, distributed systems) to be required, not optional.

Where Are Data Engineer Jobs Located, and How Remote-Friendly Are They?

Geography is more spread out for Data Engineer roles than for Data Analyst, with India taking a much larger share, a reflection of how much of the world's pipeline-building work flows through global capability centers.

Top countries by share of Data Engineer postings.

United States: 29% (US-only Data Engineer openings)
India: 23%
United Kingdom: 5%
Canada: 4%
Germany: 3%
Poland: 3%
France: 2%
Brazil: 2%
Mexico: 2%

The US is still the largest single market, but India is a closer second than it is for almost any other tech role, nearly a quarter of all Data Engineer postings. Most of those postings come through consulting and software-services firms supporting US and UK clients, which shapes both the work pattern and the salary structure (the US-only median we cited above does not apply to those listings).

The "Data Engineer is a perfect remote-first role" assumption is partly true, but onsite still leads.

Share of Data Engineer postings tagged with each work mode. Some postings carry multiple tags (e.g., "Hybrid or Remote"), so percentages sum to more than 100%.

Onsite: 50% of postings (3,458)
Hybrid: 32% (2,231)
Remote: 27% (1,848) (fully-remote Data Engineer openings)

Postings can carry multiple work-mode tags when a company says "Hybrid or Remote", which is why the percentages sum to more than 100%. Fully remote Data Engineer roles do exist and are slightly more common than they are for Data Analysts (27% vs 24%), but the dominant mode is still onsite. The remote share concentrates in product-led tech and SaaS companies; financial services, healthcare, and government default to onsite or hybrid.

Who's Hiring Data Engineers in 2026?

The top hiring companies on our board are dominated by global consulting and software-services firms supporting enterprise clients, with a handful of growth-stage tech companies and financial-services employers in the mix.

Top companies by active Data Engineer postings. Counts include all locations of the same job.

Accenture: 452 postings (global consulting)
Launch Potato: 164 (digital media)
PricewaterhouseCoopers: 161 (Big Four consulting)
Exadel: 127 (software services)
AgileEngine: 121 (software services)
Jobgether: 116 (job-aggregator/staffing)
Booz Allen Hamilton: 69 (government consulting)
Barclays: 65 (banking)
Nexthire: 55 (staffing)
Brillio: 40 (software services)
Capco: 35 (financial services consulting)
Amgen: 32 (biotech)

The strong showing from Accenture, PwC, Exadel, AgileEngine, Brillio, and Capco confirms what the geography numbers already suggested: a meaningful share of Data Engineer demand flows through consulting and services firms, not direct posts from end employers. If you're early in your career, those firms are often the easiest path in: you trade some salary upside for faster placement, broader project exposure, and structured training. Direct-post jobs from product companies tend to be more competitive but offer better long-term equity and platform-team growth. For specific company processes, our interview preparation guides break down the rounds, topic priorities, and behavioral expectations company by company.

How to Use This in Your Job Search

If you're preparing for a Data Engineer job hunt, the data points to a clear sequence.

1. Build the table stakes ruthlessly. Python, SQL, and pipeline-building are the three filters every posting applies. Not weekend-tutorial Python, production Python: writing testable modules, handling errors, packaging code that runs reliably on a schedule. Not select-star SQL, production SQL: window functions, CTEs, query plans, performance tuning. And pipeline-building means the actual pattern of extracting from a source, transforming with code or SQL, and loading into a destination, with the operational concerns (idempotency, retries, observability) baked in.

2. Pick a cloud and an orchestrator. AWS is the largest single cloud at 44%, but a candidate fluent in Azure or Google Cloud covers comparable ground in their respective company segments. Pick the one that matches the companies you actually want to work for. For orchestration, Airflow is the safest default: it shows up in 29% of postings and pairs strongly with Python (lift 1.22), but Dagster has a real salary premium and is increasingly the choice on greenfield platforms. Don't try to be expert in three orchestrators; be expert in one.

3. Add one differentiator before applying. The salary data is unambiguous: the skills companies pay the largest premiums for are not the table stakes. Spark, observability tooling, monitoring, distributed-systems experience, dbt, BigQuery, and Airflow each move your median US base salary by roughly $11K to $22K over the role baseline. Pick one that fits the kind of platform you want to build (streaming, warehouse-native, or distributed-compute) and learn it deeply enough to talk through trade-offs in an onsite.

4. Drill the topics, then practice the rounds. Reading about Data Engineer skills is easy; performing under interview conditions is the hard part. Our interview-prep courses cover the foundations across SQL, Python, and system design. The question bank lets you drill SQL, data modeling, distributed systems, and system design topics one at a time. AI mock interviews let you practice the full round under realistic conditions, with on-demand feedback on data-modeling and pipeline-design questions specifically.

5. Filter the job board for your stack. Browse current Data Engineer openings on the InterviewStack.io job board and combine role and skill filters to narrow to your exact stack, e.g., Data Engineer + Snowflake + dbt or Data Engineer + Spark + AWS. The board updates daily, so the listings are current.

FAQ

Q. What skills do companies want for Data Engineer roles in 2026?

Python, SQL, and pipeline-building are table stakes, appearing in roughly seven out of ten postings. Above that base, AWS (44%), Data Quality (43%), Data Modeling (38%), and the modern data stack (Snowflake 31%, Databricks 29%, Airflow 29%, dbt 24%) sit in the common tier. Streaming and infrastructure skills like Kafka, Apache Spark, Kubernetes, and Terraform are differentiators that pay real premiums.

Q. What is the median salary for a Data Engineer in 2026?

The median US base salary across 1,183 Data Engineer postings with disclosed salary data is $128,300. That figure excludes equity, bonuses, and sign-on, so total compensation at top employers runs meaningfully higher, especially in tech and finance.

Q. Which Data Engineer skills pay the highest premium over the role baseline?

Among US postings, the largest premiums attach to streaming, infrastructure, and modern data stack specialties. Distributed Systems, Data Lake, Looker, Docker, S3, and A/B Testing all sit at $150,000 (about $22K above the $128,300 baseline). Apache Spark ($148K), Observability ($148K), Monitoring ($147K), dbt ($140K), Scala ($140K), BigQuery ($140K), and Airflow ($139K) follow with premiums in the $11K to $20K range.

Q. Is Data Engineer a good entry-level role to break into?

It is one of the harder roles to enter. Only 3% of Data Engineer postings are explicitly entry-level (219 of 6,877), compared with 8% for Data Analyst. Companies overwhelmingly expect production pipeline experience, so career switchers typically route through analytics-engineer, backend-engineer, or junior-analyst roles before stepping in.

Q. Where are most Data Engineer jobs located, and how remote-friendly are they?

The United States is the largest single market at 29% of postings, followed closely by India at 23%. The UK (5%), Canada (4%), Germany (3%), and Poland (3%) round out the top six. About 27% of postings are tagged remote, 32% hybrid, and 50% onsite (some postings carry multiple tags), so onsite is still the dominant default.

Q. Which companies hire the most Data Engineers in 2026?

Global consulting and software-services firms dominate the top of the list: Accenture (452 active postings), Launch Potato (164), PricewaterhouseCoopers (161), Exadel (127), AgileEngine (121), Jobgether (116), Booz Allen Hamilton (69), Barclays (65), Nexthire (55), Brillio (40), Capco (35), and Amgen (32).

Q. What is the dominant Data Engineer skill stack in 2026?

Python plus SQL is the foundation, appearing together in 4,002 postings (58% of the market) with a co-occurrence lift of 1.15. The most over-represented combinations layer Airflow, CI/CD, and Apache Spark on top of that base: Airflow + Python (lift 1.22), Airflow + Data Pipelines (1.20), CI/CD + Python (1.20), and Apache Spark + Python (1.19) all show stacks built around an orchestrator and pipeline-as-code discipline.

Final Thoughts

The Data Engineer role in 2026 is one of the most consistently defined and best-compensated jobs in tech, with a clear stack (Python plus SQL plus pipelines plus cloud plus orchestrator) and a real ladder above it. The trade-off is that the entry-level door is unusually narrow: companies want production experience and aren't budgeting much to train it. If you can route through an analytics-engineer or backend-engineer role to build the production-pipeline reps, the senior tier opens up quickly, and the differentiator skills compound from there.

We'll refresh this analysis quarterly so the trend lines stay current.