📕 Dataford Job Market Report

Dataford — Thu, 18 Jun 2026 22:05:04 +0000

We run Dataford, an interview prep platform. People come to us to study for specific companies and specific roles before they ever sit in the interview. That gives us a clear view of where the market is heading.

We do not see who got hired. We see who is getting ready. And what people get ready for tends to move before the hiring data does.

Between January and June 2026, more than 200,000 candidates ran over 231k sessions on Dataford. We looked at which companies they studied for, which roles they targeted, and at what level. Here is what we got for you.

By the way, this is candidate behavior on one platform, not a hiring census, so please treat it as an early read, not the universal truth.

The five things worth knowing

If you only read one section, I'd recommend to read this one.

Nvidia is the most studied-for company by a wide margin, and the AI labs now sit right next to Big Tech at the top.
Software Engineer is still the top role, taking over 22% of all roles.
AI Engineer is now the #2 role overall. It passed Data Analyst, Data Engineer, and Data Scientist this year. Individually.
Classic data titles are sliding in share while anything labeled AI is gaining a lot of traction.
A new batch of AI-native titles showed up mid-year that were basically nonexistent in the beginning of the year.

Now that you got the TLDR.
Let's get into more details.

What are the top companies hiring today

Nvidia is number one, and not by a little. Here is the top 15 by prep volume.

Company	Sessions
Nvidia	2,816
Google	2,108
Apple	1,916
Amazon	1,178
OpenAI	1,143
Databricks	1,014
Anthropic	974
Anduril	965
Accenture	932
Meta	926
Capital One	851
Microsoft	793
Ramp	695
American Express	679
Adobe	643

Look at the top eight. A few years ago that block was only FAANG. Now Nvidia leads it, and OpenAI, Databricks, and Anthropic are all sitting in there. Anduril, a defense company, is in the top eight too. You probably heard that terms MANGOS (Meta, Anthropic, Nvidia, Google, OpenAI, SpaceX).

Well this is happening!

The AI labs are becoming the new hot sector

Sector	Sessions
Big tech	12,100
Finance	5,400
AI labs	4,900
Consulting	3,000
Defense and aerospace	2,300

The AI labs are tiny compared to the banks and the consultancies by raw employee count. But what's insane, it's that as a group they still pull more prep than all of consulting.

People are chasing the companies they think are building the future and AI labs are definitely one of them in 2026. Defense is also growing led by Anduril whereas Lockheed Martin, Northrop Grumman are losing ground.

Software Engineer Role is still leading the pack

Before we get to the AI story, software Engineer is still the king, accounting fo 22% of every single prep session on the platform.

Role	Sessions
Software Engineer	12,932
AI Engineer	5,007
Data Analyst	3,858
Data Engineer	3,838
Data Scientist	3,791
QA Engineer	3,038
Product Manager	2,320
Solutions Architect	1,838
Machine Learning Engineer	1,784
Project Manager	1,742
Business Analyst	1,715
Account Executive	1,510

If you stack up everything in the engineering family, it is 55% of demand. More than half of all candidates are aiming at some flavor of engineering roles.

AI Engineer is the hottest role in the market

AI Engineer is the #2 role on the entire platform. It now pulls more prep than Data Scientist, more than Data Analyst, and more than Data Engineer. Not combined. Each one, on its own.

AI Engineer       5,007  ████████████████████
Data Analyst      3,858  ███████████████
Data Engineer     3,838  ███████████████
Data Scientist    3,791  ███████████████
ML Engineer       1,784  ███████

Two years ago "AI Engineer" was barely a real job title. Now it is second only to the most generic engineering role there is. That is a fast climb.

What is gaining share (the new hottest roles)

Across the first half versus the second half of the year, here is what moved, measured in points of share so that overall traffic growth does not fake a trend.

Gaining share

Software Engineer: +3.4
Research Scientist: +1.2
AI Engineer: +0.9
Embedded Engineer: +0.9
Operations Manager: +0.9
Account Executive: +0.8
Data Engineer: +0.6

Losing share

Product Manager: -0.6
QA Engineer: -0.6
Machine Learning Engineer: -2.6
Data Scientist: -3.1
Data Analyst: -4.7

The takeaway is not "data is dead." There is still huge demand for analysts and scientists. The energy is just relocating to roles with AI in the name, and the fastest bridge from analytics to that energy is short.

Job titles that did not exist and are popping in H2' 2026

This was our favorite thing in the dataset. A cluster of roles appeared in the second half of the year that were essentially zero at the start of it.

Forward Deployed Engineer: 229
GenAI Engineer: 215
Agentic AI Engineer: 125
AI/ML Analyst: 82
AI Research Scientist: 23
AI Product Manager: 16
Prompt Engineer: 12

Together they are still only about 1% of demand, so nobody should panic-rebrand their LinkedIn tonight. But THIS is growing and I won't be suprised to see these roles at the top in 2027 and 2028.

Forward Deployed Engineer is the one we would watch hardest. It came out of the AI labs and enterprise AI world, and it is the role for people who can build with AI and sit in front of a customer. That combo is rare and it will be a huge role in our opinion in the next decade.

Management is losing ground to IC (Individual Contributors)

Individual contributor roles: ~83%
Management track: ~11%
Senior or strategic IC: ~6%

More than four out of five sessions are aimed at hands-on IC seats. When demand tilts that hard toward IC, it usually means companies are hiring people to do the work, not just to manage it.

So what should you actually do

Short version, by where you are starting from.

If you are a data analyst or data scientist, do not fight the shift, ride it. Move toward AI/ML Analyst and AI Engineer work. The skills transfer and the demand is moving your way.

If you are a software engineer, you are in the biggest and safest pool, and also the most crowded one. AI Engineer is where the same skill set earns a premium right now.

If you are early career, aim at the clusters with momentum, not only the famous logos. The AI labs and defense companies pull demand far above their size and draw a smaller crowd per opening.

Whatever you are, the center of gravity moved in 2026, and it moved toward AI.

Caveats from this analysis

This is interview-prep behavior on one platform. It is an early demand signal, not a count of open roles or hires.
We derived company and role from the guide pages people opened, using the roughly 58,000 sessions where both were cleanly attributable out of the 231,000+ total.
Traffic grew across the year, so all trend numbers are reported as change in share of the mix, not raw growth, to avoid mistaking platform growth for market demand.
Company-level quarter-to-quarter shifts are noisier than role-level ones, so company rankings use full-period totals.
Everything is current through mid-June 2026.

If you want to see more insights like these, let us know in the comments! Would love to get your feedback as well, as this is #1 priority for us.

Thank you for reading!

If you need to prep for any tech interview, don't forget to head into our platform, we have over 50k+ interview guides to help you ace your next interviews

Interview Prep Built by Interview Experts (Dataford.io)

Dataford — Sun, 14 Jun 2026 15:18:59 +0000

Hi. We're Dataford, and this is our first post here, so let us introduce ourselves.

We build interview preparation by practitioners, for practitioners. Not recruiters, not content farms. Company-specific guides and question banks across 50+ roles, spanning the data, engineering, product, and go-to-market jobs that define the next decade of tech. You can browse the whole library at dataford.io/interview-guides.

Why we exist

Being great at the job and being great at the interview are two different skills.

Most interview prep is a pile of generic questions and recycled advice. It tells you what an interview looks like in the abstract, but never what this team, at this company, for this role is actually screening for.

That gap is where good people get filtered out. Closing it is the whole point of Dataford.

Our mission is short to say and hard to do: help people land the roles they are actually capable of.

Where this came from

Before Dataford, our founder Amney spent years inside the kind of teams candidates dream of joining, most recently as a Lead Product Growth Analyst at Meta.

The more interviews she sat on the other side of, the clearer one pattern became. The person who would be excellent at the job is not always the one who interviews well, and the difference usually comes down to information no one ever hands them. What does this company actually test? How much does communication count against the technical work? Which "obvious" answer quietly costs you the round?

Insiders know. Everyone else guesses. Dataford exists to give that insider context to everyone.

How we help

We map each role to the competencies that actually decide the outcome, then build prep that mirrors the real room. Here is what that looks like in practice.

Company and role specific guides. Pick your target role and company and see what they screen for, how their process runs, and the kinds of questions they really ask. Start with the guides library.

Real, role-tagged practice questions. Thousands of SQL, Python, and behavioral questions, weighted the way they are weighted in real loops, with AI evaluation so you get feedback instead of just a right-or-wrong.

Mock interviews. Timed, realistic runs with detailed per-question feedback, so the first time you feel the pressure is not the day it counts.

Hands-on courses. Structured, practice-first courses with a certificate at the end, built by people who have done the work, not summarized it.

A roadmap that fits you. A personalized path based on your target role and experience level, so you spend your time on the fifty questions that matter instead of a thousand that do not.

What we believe

Four principles shape everything we ship.

Specificity beats volume. The right fifty questions for your role beat a thousand generic ones.

Prep should mirror reality. Our questions and weightings reflect how companies actually hire, not an idealized version that lives only in study guides.

Honesty over hype. No fake guarantees and no inflated promises. We tell you what the bar is and give you an honest shot at clearing it.

Built by practitioners. If we have not lived it or verified it, it does not make the cut.

That is us. If you are prepping for a specific role at a specific company, find your guide at dataford.io/interview-guides, and build your prep plan here.

We will be posting here regularly: real prep, honest takes on how hiring works, and what we are learning as we build. Follow along, and tell us in the comments what role you are targeting. We read every reply.

DEV Community: Dataford