DEV Community: Alex Bell

Capgemini and Accenture Score Lowest of 7 Consulting Firms: Live Interview Data

Alex Bell — Wed, 08 Jul 2026 13:52:05 +0000

iOS Developer Interview Scores Lower Than Software Engineer. The Data Explains Why.

Alex Bell — Fri, 03 Jul 2026 11:16:33 +0000

iOS Developer Interviews Score Lower Than Software Engineer. The Data Shows Why.

Most candidates preparing for tech interviews assume Software Engineer roles have the hardest interview process. The data from Final Round AI's Interview Copilot, which captures live interview sessions across multiple companies and roles, tells a different story.

Final Round AI analyzed 83,421 live interview session records across 14 standardized tech roles from October 2022 to September 2025. The metric is a 0-to-100 score measuring how complete and well-structured candidates' answers were during actual job interviews.

iOS Developer averaged 50.6. Software Engineer averaged 54.3. Product Manager averaged 59.0.

The Full Role Ranking

From lowest to highest average answer score:

iOS Developer: 50.6 (413 sessions)
Engineering Manager: 53.0 (525 sessions)
Site Reliability Engineer: 53.9 (1,736 sessions)
Software Engineer: 54.3 (20,955 sessions)
Data Analyst: 54.5 (4,466 sessions)
QA Engineer: 54.8 (5,753 sessions)
Security Engineer: 55.3 (4,255 sessions)
Data Engineer: 55.4 (14,201 sessions)
DevOps Engineer: 55.5 (13,740 sessions)
Machine Learning Engineer: 56.7 (1,861 sessions)
Data Scientist: 57.8 (4,676 sessions)
Cloud Engineer: 58.1 (889 sessions)
Product Manager: 59.0 (2,814 sessions)

The dataset average across all 14 roles is 55.3.

Why iOS Developer Scores So Low

iOS interview questions require deep knowledge of Swift, UIKit, SwiftUI, and Apple-specific frameworks. Candidates who prepare with general software engineering resources (LeetCode, system design guides) arrive under-prepared for that platform-specific depth. The gap between general SWE prep and iOS-specific prep is the primary driver of the 50.6 average.

This is not an abstract difficulty gap. In live sessions, the questions that produce the lowest iOS scores are not algorithmic puzzles but platform-specific ones: how UIKit manages view lifecycle, how Grand Central Dispatch handles concurrency, how SwiftUI state propagates through a view hierarchy. Standard prep guides do not address these in depth.

Why Product Manager Scores Highest

Product Manager at 59.0 contradicts the common narrative that PM interviews are among the most difficult. PM candidates give more complete, better-structured answers than any other role in the dataset.

The explanation is preparation ecosystem quality. Amazon's Leadership Principles have an entire prep industry built around them. Google's STAR-format behavioral questions have thousands of documented candidate examples. PM candidates use frameworks like CIRCLES and STAR that structure their answers specifically for the questions asked. The 59.0 score reflects better preparation alignment, not easier interviews.

Software Engineer Scores Are Declining

Software Engineer averaged 55.6 in 2023, 54.5 in 2024, and 53.0 in 2025. That 2.6-point decline across three years is the most significant trend in the role dataset.

Two factors likely contribute. First, the technical bar at top companies (Amazon, Google, Meta) has increased since 2023. Behavioral rounds require more specific, measurable outcomes in STAR answers. Second, more candidates are using Interview Copilot in live sessions without prior structured preparation, pulling the average down.

Data Engineer, by contrast, stayed consistent at 55.3 to 55.9 across the same three years. The prep ecosystem for Data Engineering has stabilized around SQL, data pipeline design, and cloud infrastructure questions.

What This Means for How You Prep

For iOS Developer candidates: build at least two iOS-specific projects you can walk through in full architectural detail. Focus prep on Swift-specific language features and Apple framework patterns, not only general algorithms.

For Software Engineer candidates: the 2023-to-2025 decline suggests behavioral prep is now as important as LeetCode prep. If you are targeting Amazon, Google, or Meta, your STAR answers need specific, quantified outcomes, not general team contributions.

For Engineering Manager candidates: the 53.0 score reflects a gap specifically in how EM candidates answer leadership and conflict-resolution questions. The behavioral dimension of EM interviews is where most candidates score lowest, not the technical one.

The full dataset with charts breaking down all 14 roles is in Final Round AI's research report: https://www.finalroundai.com/blog/tech-role-interview-difficulty-data

The Hardest Tech Company Interviews Are Not Where You Think (Data from 59,000 Live Sessions)

Alex Bell — Wed, 01 Jul 2026 11:26:34 +0000

The Company Everyone Preps For Isn't the Hardest One

Most tech candidates spend the bulk of their interview prep on Google and Amazon. Final Round AI's Interview Copilot captures data from actual job interviews, not practice sessions, and the company difficulty ranking that comes out of 59,505 records across 23 major tech companies tells a different story.

Salesforce averages 50.7 out of 100. Google averages 56.8. Amazon averages 57.5.

That means Salesforce scores 6.1 points harder than Google and 6.8 points harder than Amazon based on how well candidates answered questions during live interview sessions. Oracle (51.5) and Cloudflare (51.3) also score harder than every FAANG company in the dataset.

How the Scores Work

Interview Copilot runs during real job interviews, capturing each question and scoring the answer on completeness, structure, and relevance on a 0 to 100 scale. A score of 100 means a comprehensive, well-structured response. Below 40 is typically a short or incomplete answer. The dataset covers October 2022 to September 2025, live sessions only, no practice data.

This is not a survey of how hard candidates felt the interview was. It is a measure of how complete their answers actually were.

The Full Ranking (23 companies, 200+ records each)

Hardest to most approachable:

Salesforce: 50.7 (1,062 records)
Cloudflare: 51.3 (229 records)
Oracle: 51.5 (1,393 records)
Atlassian: 54.6 (721 records)
AMD: 55.0 (763 records)
Adobe: 55.4 (308 records)
TikTok: 55.5 (770 records)
Meta: 55.5 (3,220 records)
Stripe: 55.6 (236 records)
LinkedIn: 56.1 (308 records)
DoorDash: 56.2 (294 records)
Apple: 56.3 (4,528 records)
IBM: 56.5 (2,136 records)
Google: 56.8 (16,604 records)
Databricks: 57.2 (322 records)
Amazon: 57.5 (18,932 records)
Microsoft: 57.8 (4,921 records)
Netflix: 59.2 (280 records)
ServiceNow: 59.4 (546 records)
Workday: 61.0 (448 records)
Uber: 70.3 (189 records)

The dataset average across all 59,505 records is 56.8. Salesforce sits 6.1 points below that.

The FAANG Finding

All five FAANG companies sit within a 2.3-point band near the dataset middle. None of them is in the top or bottom tier. The hardest tier belongs to enterprise software: Salesforce, Oracle, Cloudflare.

Why? Two reasons likely drive this.

First, Salesforce and Oracle interviews test platform-specific and database-specific knowledge that standard software engineer prep does not address. Salesforce interviews go deep on CRM architecture and Salesforce-specific cloud platform behavior. Oracle interviews test SQL optimization and enterprise RDBMS internals at a depth that LeetCode preparation does not build.

Second, the prep ecosystem for Google and Amazon is enormous. There are tens of thousands of tagged LeetCode problems, YouTube mock interview recordings, and Leadership Principle prep guides for Amazon. Far fewer resources exist specifically calibrated to Salesforce or Oracle interview formats. Candidates arrive less specifically prepared, and the answer scores reflect that.

The Uber Anomaly

Uber sits at 70.3, the highest in the dataset and 19.6 points above Salesforce. That gap is notable. With 189 records, Uber has the smallest sample of any company included, which reduces confidence compared to Amazon (18,932) or Google (16,604). The directional finding is interesting but should be treated with more caution than the high-volume results.

What to Do with This Data

If you are targeting Salesforce or Oracle: the data suggests you need more platform-specific preparation than most general interview guides provide. For Salesforce, this means Salesforce architecture, CRM data models, and value-based behavioral prep aligned to their core values. For Oracle, this means database internals at a depth that goes beyond standard SQL interview prep.

If you are targeting Google or Amazon: the data suggests you are probably not underprepared if you have been doing standard FAANG prep, but the bar for what counts as a complete answer is high because every interviewer has seen thousands of structured responses. The distinction between a 55 and a 65 answer at these companies often comes down to specificity and concrete outcome details.

The full ranking with charts and methodology is in Final Round AI's report: https://www.finalroundai.com/blog/hardest-tech-company-interviews-ranked

PM Prep Gets This Wrong: Behavioral Questions Score Highest in Live Interviews, Not Metrics

Alex Bell — Tue, 30 Jun 2026 13:17:40 +0000

The PM Prep Advice That the Data Contradicts

Most product manager interview prep resources treat metrics and analytics as the hardest round and behavioral as the one that takes care of itself with a bit of STAR practice. Final Round AI's data from 480 live PM interview sessions tells a different story.

Behavioral questions average 67.7/100 in live sessions. Metrics and analytics questions average 65.8/100. Behavioral scores highest. Metrics scores lowest of the high-volume question types.

The gap is 1.9 points. That sounds small. But it holds consistently across sessions, companies, and role levels — and it directly contradicts where most PM candidates allocate their prep time.

The Numbers: 10,374 Responses from 480 Live PM Sessions

The dataset covers 10,374 interview question responses from 480 live product manager sessions captured through Final Round AI's Interview Copilot between October 2022 and September 2025. Each response receives a score from 0 to 100 reflecting the quality and completeness of the verbal answer.

Question types were classified by transcript keywords:

Question Type	Responses	Average Score
Behavioral	686	67.7 / 100
Strategy / Prioritization	399	66.2 / 100
Metrics / Analytics	231	65.8 / 100
Estimation	63	50.4 / 100

Estimation shows the largest gap from the behavioral benchmark, but with only 63 responses it is below the 100-response threshold for category-level conclusions. The directional finding is consistent with brief-answer question types scoring lower across the broader dataset.

Why the Gap Exists

This is not a finding about which question type is objectively harder. It is a finding about how PM candidates structure their verbal answers in live sessions.

Behavioral questions are answered using STAR format by design. The structure forces candidates to state a specific context, describe concrete actions, and land on a measurable outcome. The scoring model rewards all four elements. Most PM candidates have practiced STAR enough that the structure comes out in the room.

Metrics questions break differently. The typical live-session metrics answer runs through a framework ("AARRR: acquisition, activation, retention, referral, revenue") and then stops without a stated hypothesis or a concrete recommended action. The framework knowledge is correct. The verbal completeness is missing. The scoring model reads an incomplete answer even when the analytical instinct behind it is right.

The fix is not more framework knowledge. It is the habit of stating the hypothesis before the framework: "My hypothesis is that the drop is seasonal and concentrated in mobile. Here is how I would check that." That opening sentence gives the interviewer your analytical conclusion before your process. It scores higher in live sessions because it is a complete verbal response — hypothesis, method, expected finding, recommendation — not a recitation.

Amazon Outscores Google in Live PM Sessions

Among FAANG companies with 500 or more classified responses in the dataset:

Amazon PM sessions: 61.4/100 (756 responses)
Google PM sessions: 58.7/100 (553 responses)
Meta PM sessions: 53.3/100 (350 responses — directional, below 500-response threshold)

Amazon scores highest despite having one of the most demanding PM loops. The likely explanation is structural: Amazon's Leadership Principles framework forces candidates to anchor every answer to a named principle before the story. That anchor acts as a thesis statement, and the answer then covers situation, action, and outcome in relation to it. The LP framework improves verbal completeness across all rounds — not just behavioral.

Google PM sessions average 58.7/100. Google's loop places heavy emphasis on analytical and product strategy rounds, which score lower than behavioral rounds in this dataset. The gap suggests that Google PM candidates who invest most of their prep in frameworks and under-prepare behavioral stories are showing up in the data exactly as you would expect.

Meta PM sessions average 53.3/100. Meta's PM behavioral rounds are calibrated to company values (Move Fast, Be Direct, Long-Term Impact) rather than general competency. Candidates who open with the value they are demonstrating rather than building to it in the last 15 seconds of the story score measurably higher.

The Specific Question Type That Matters Most

Among PM-specific questions appearing 10 or more times in the dataset, "How do you prioritize features for a product roadmap?" averages 62.5/100 across 14 sessions. Below the 20-session question-level threshold, but directionally consistent with the broader strategy and prioritization category.

The failure pattern is predictable: the candidate names the framework, applies it to a generic example, and stops before stating which item they would actually ship first and why. The scoring model reads an incomplete answer. The interviewer asks a follow-up because the answer never arrived at a decision.

Prioritization questions in PM interviews are not asking for a demonstration of framework knowledge. They are asking for a demonstration of judgment. "I would use RICE scoring" is the beginning of an answer. "I would deprioritize X despite its high reach because the effort is disproportionate to the retention delta, and ship Y first because it is the only item in this batch that directly addresses the activation drop we saw last quarter" is an answer.

The Three Prep Changes That Move PM Scores

1. State the hypothesis first on every metrics question. Before any framework, before any analysis, name what you think is happening. Candidates who do this score 2+ points higher on metrics questions in live sessions than candidates who start with the framework.

2. Map Amazon stories to LPs before the loop, not during. Every story needs a named principle as its anchor. Not just Customer Obsession and Ownership — include Frugality, Learn and Be Curious, and Dive Deep, which Amazon PM interviewers probe specifically for senior roles.

3. Practice prioritization questions with a forced conclusion. After naming the framework, force yourself to name one item that would not ship and explain why. The scoring gap in prioritization questions is almost entirely concentrated in candidates who run the analysis but never deliver the decision.

The full breakdown with charts — including the company-level comparison and question-type scores — is in Final Round AI's full research report: PM interview question data from 10,000+ live sessions

Tech Candidates Score Highest on System Design and Lowest on Coding. The Data Explains Why.

Alex Bell — Mon, 29 Jun 2026 06:11:24 +0000

You're Probably Spending Too Much Prep Time on the Wrong Interview Round

The most common advice in tech job prep communities: "System design will make or break your loop." It gets repeated so often it becomes conventional wisdom. Engineers spend weeks building knowledge trees for distributed systems, caching strategies, and database sharding, not because they know system design is hardest for them, but because everyone says it is.

Final Round AI's data across 38,183 classified live interview sessions tells a different story. System design questions produce the highest average verbal scores of any question type. Technical coding questions produce the lowest.

This does not mean system design is technically easy. It means candidates explain system design answers more completely than they explain coding solutions, at least verbally, during live interviews. The distinction matters a lot for how you allocate prep time before a Google or Meta loop.

The Numbers: 38,183 Classified Sessions from 816,000+ Real Interviews

The dataset covers 816,927 live interview sessions captured through Final Round AI's Interview Copilot between October 2022 and September 2025. Interview Copilot listens during actual job interviews and records the candidate's verbal responses. Each response receives a score from 0 to 100 reflecting the quality and completeness of the verbal answer, as assessed by Final Round AI's AI evaluation model.

To classify question types, sessions were categorized by transcript keyword matching:

Behavioral: "tell me about a time", "describe a situation", "give me an example"
System design: "design a system", "architecture", "distributed system"
Technical coding: "algorithm", "time complexity", "data structure", "implement a function"

Results after filtering to 38,183 classified sessions:

Question Type	Sessions	Average Score
System Design	6,092	65.3 / 100
Behavioral	29,458	62.0 / 100
Technical / Coding	2,633	61.1 / 100

The weighted average across all three types is 62.5/100. System design scores 4.2 points above technical coding. The gap between behavioral and coding is smaller at 0.9 points but consistent across companies and roles.

Why This Gap Exists

This is not a finding about which round is technically harder. It is a finding about verbal communication behavior in live interview settings.

System design interviews consist entirely of verbal explanation. Candidates describe architecture choices, trade-offs, scalability decisions, and component interactions. The verbal record is naturally long and structured. Even a candidate who is uncertain about the right database choice will typically narrate multiple options and explain why they are weighing them. That narration scores well.

Technical coding interviews have objectively correct answers. Candidates often state the approach briefly and then code silently. "I'd use a hash map" is technically accurate but scores low because it is not a complete verbal explanation. A candidate who says "I'd use a hash map here because lookups are O(1) and we are making repeated key lookups across a dataset that does not change during iteration, so using a list would make this O(n) per lookup and the problem constraints make that too slow" scores significantly higher, because the evaluation model rewards verbal completeness.

The behavior driving the gap: candidates narrate system design in full sentences with trade-offs explained out loud. They narrate coding solutions in sentence fragments, then code silently. The fix for coding rounds is to import the narration habit from system design prep into coding prep.

The Google Finding Is the Most Surprising

Across Amazon, Google, Meta, and Apple sessions with 100 or more sessions per question type, Google shows the starkest split between question types:

Google system design: 71.3/100 (154 sessions)
Google behavioral: 62.8/100 (945 sessions)
Google technical coding: 62.5/100 (196 sessions)

That is an 8.5-point spread between system design and behavioral. If you are preparing for a Google loop and spending equal time on all three round types, you are under-investing in behavioral stories. System design is already where Google candidates score highest. Behavioral is where they score lowest, and where additional prep produces the most measurable gain.

Amazon shows a completely different pattern. Amazon behavioral sessions average 64.9/100 (3,099 sessions) and system design averages 65.2/100 (252 sessions). The gap is just 0.3 points. Amazon candidates appear to calibrate verbal completeness across question types more evenly, which likely reflects the Leadership Principles framework. When every behavioral story maps to a named principle like Ownership or Customer Obsession, the verbal structure stays consistent across rounds, and that consistency transfers to non-behavioral questions too.

Meta behavioral sessions score the lowest of any FAANG company at 59.2/100 across 315 sessions. Meta's interview culture values directness and speed over narrative completeness. Candidates who deliver long context-heavy STAR stories before arriving at the impact tend to score lower at Meta than at Amazon or Google, even with equivalent underlying experience.

The Specific Behavioral Questions Where Candidates Score Lowest

Among behavioral questions with at least 20 sessions in the dataset, the lowest-scoring substantive question is:

"Tell me about a time when your communication skills helped you at your job" scored 52.4/100 across 175 sessions.

That is 9.6 points below the behavioral category average of 62.0/100. This question appears across nearly every role and company. It is not niche. Yet candidates underperform it by nearly 10 points relative to the average.

Other behavioral questions with low scores (14 or more sessions):

"Tell me about a time when you made a mistake" scored 48.3/100 (21 sessions)
"Tell me about a time when you were in charge of a project with a deadline" scored 47.3/100 (21 sessions)
"Tell me about a time that you were under huge pressure" scored 50.0/100 (14 sessions)

The pattern across these low-scoring questions is consistent: they ask for self-awareness, accountability, or interpersonal skill rather than achievement. Candidates score higher when the behavioral story ends with a clear quantifiable win. When the question asks for a failure, a conflict, or a sustained pressure situation, verbal completeness drops because candidates hedge, minimize, or rush to the resolution without building enough context.

For the communication skills question: the reason candidates score 52.4/100 across 175 sessions is vagueness. They describe "a situation where communication was important" instead of naming a specific stakeholder, a specific decision, a specific outcome with a number. The specificity of the scenario, including who the conversation was with, what was at stake, what channel was used, and what the measurable outcome was, is what separates a 52 from a 70 on this question. A strong answer names a product team, an engineering lead, a release decision, and a number. A weak answer names "a situation where communication broke down" with no named parties and no stated result.

What to Do With This Data

For Google candidates: System design is already working. Google system design sessions average 71.3/100, the highest of any company-type combination in this dataset. The behavioral gap is where your loop is most at risk. Build three to four strong stories for failure narratives, communication skill situations, and deadline scenarios. Each story should run 90 to 120 seconds, name a specific person or team, and quantify the result.

For Amazon candidates: The Leadership Principles framework is working. Amazon behavioral rounds average 64.9/100, the highest FAANG behavioral average in this dataset. Keep mapping every story to a specific principle before the interview, including the less commonly drilled ones like Frugality, Learn and Be Curious, and Dive Deep. The structure lift from LP mapping applies even when the question does not name a principle explicitly.

For Meta candidates: Lead with the impact. State the outcome in the first 15 seconds. If you reach 30 seconds into an answer before naming a result, start over. Meta behavioral sessions score 59.2/100, the lowest FAANG behavioral average. The correction is faster delivery of each story's result, not more stories.

For coding rounds across all companies: The single highest-leverage habit is to narrate your reasoning before writing code. After identifying your approach, explain why before touching the keyboard. Walk through edge cases verbally. State the time and space complexity before writing the first line. Candidates who build this narration habit consistently score closer to the behavioral average than the technical coding average.

Why This Data Is Different From Most Interview Difficulty Research

Most research on interview difficulty relies on self-reported candidate ratings or employer surveys. Glassdoor difficulty ratings, for example, are based on candidates selecting "easy", "medium", or "difficult" after the fact, which reflects emotional difficulty rather than performance. Final Round AI's dataset reflects actual response quality in the moment, scored by the same AI evaluation model across all sessions. It is not a survey. It is performance data from 816,927 real interviews.

That distinction matters for how to interpret the findings. When this dataset says technical coding questions score 61.1/100 on average, it means candidates gave less complete verbal explanations for those questions in live conditions. It does not mean they failed the round or that coding problems are objectively easier to solve. It means the verbal articulation of their reasoning was less thorough than it was in system design and behavioral rounds. That is the gap this data surfaces, and it is the gap that is fixable with practice.

The full breakdown with charts, including the Google question-type split and the specific behavioral question scores, is in Final Round AI's full research report: interview question type scoring across 38,183 live sessions

Goldman Sachs IB Associates Score 39.7 Out of 100. The Data Explains Why.

Alex Bell — Thu, 25 Jun 2026 08:10:37 +0000

Goldman Sachs Investment Banking is supposed to be the hardest finance interview. The data says something different.

Final Round AI analyzed 9,494 live interview sessions across JP Morgan, Goldman Sachs, Capital One, Wells Fargo, Bank of America, and American Express — all captured through their Interview Copilot tool during actual job interviews, not practice runs. The results challenge most of the conventional wisdom about which finance company is hardest to interview at.

The counterintuitive finding: Wells Fargo outscores Goldman Sachs

The full company ranking by average answer quality score (0-100 scale):

Wells Fargo: 58.1/100 across 1,498 sessions
Capital One: 56.5/100 across 1,883 sessions
Goldman Sachs: 56.3/100 across 1,423 sessions
American Express: 56.2/100 across 1,309 sessions
JP Morgan: 55.9/100 across 1,981 sessions
Bank of America: 55.7/100 across 1,400 sessions

Wells Fargo, often treated as second-tier, produces the highest candidate answer scores in the dataset. Goldman Sachs sits third. JP Morgan, the largest bank by session count, ranks fifth.

The scoring model measures answer completeness and structure, not interview outcomes. It rewards STAR-format answers and penalizes vague or incomplete ones. For context: Amazon averages 57.5 and Google 56.8 in the same dataset. Finance and tech interviews are essentially equivalent. Treating finance as an easier track than a Google loop is not supported by this data.

The Goldman Sachs IB finding is the real story

Goldman Sachs Investment Banking Associate candidates averaged 39.7 out of 100 across 308 sessions. That is 16.4 points below Wells Fargo company-level average and 27.9 points below Goldman own Technology Cyber Risk Advisory role (67.6 across 161 sessions).

That gap is not about candidate quality. It is about question type.

When a Goldman IB interviewer asks you to walk through a leveraged buyout, the correct answer is two to three sentences: entry multiple, debt structure, exit assumptions. Not a narrative. The scoring model Final Round AI uses is calibrated for extended, structured responses. Brief, technically precise answers score low on that model regardless of accuracy. Candidates preparing for Goldman IB with general behavioral coaching are optimizing for the wrong format.

Role breakdown:

Goldman Sachs IB Associate: 39.7/100 (308 sessions)
Goldman Sachs Technology Cyber Risk Advisory: 67.6/100 (161 sessions)
JP Morgan Software Engineer: 56.2/100 (280 sessions)
JP Morgan Associate: 51.2/100 (119 sessions)
American Express Machine Learning Engineer: 71.0/100 (147 sessions)
Bank of America Middleware Engineer: 45.8/100 (112 sessions)

The spread within companies is wider than the spread across companies. The role matters more than the firm when calibrating preparation.

What changed from 2024 to 2025

Capital One improved 3.5 points (55.7 to 59.2). American Express improved 4.9 points (54.8 to 59.7). Both moved upward across 3,192 combined sessions.

Wells Fargo dropped 5.8 points (59.1 to 53.3) across 1,498 sessions. The largest single-year swing in either direction. JP Morgan declined 1.9 points. Goldman was flat.

If you are interviewing at Wells Fargo this year, do not calibrate from 2024 data. The 2025 figure of 53.3 puts Wells Fargo at the bottom of all six companies for that year.

What this means in practice

For Goldman Sachs IB Associate candidates: behavioral coaching is not enough. Build a parallel technical track. Practice explaining a three-statement model in under 90 seconds. The format that works for tech and risk roles actively hurts performance in IB-specific questions.

For Capital One Data Engineer candidates (196 sessions, avg 61.1): the improving trend and above-average scores validate the technical-plus-behavioral format. SQL, system design, and STAR behavioral prep is the right combination.

For Bank of America Middleware Engineer candidates (112 sessions, avg 45.8): this role scores 10 points below the BofA company average. Practice out loud. Push for specific, outcome-driven answers.

For Wells Fargo candidates in 2025: use 53.3, not 58.1.

Final Round AI full report with difficulty ranking chart and year-over-year trend data: https://www.finalroundai.com/blog/finance-company-interview-difficulty-ranking

NVIDIA SWE Onsite Broke My FAANG Prep (And Here Is What Actually Works)

Alex Bell — Thu, 25 Jun 2026 07:43:25 +0000

The interviewer dropped a block of C++ on the whiteboard and asked me to find the memory leak. Not just find it: explain why it leaked, what the performance cost was at scale, and what the GPU-specific implications were for a parallel workload. I had practiced 300+ LeetCode problems. None of that mattered in that room.

The 3% Accept Rate Is Not a Typo

NVIDIA SWE offers have hovered around a 3% acceptance rate across onsite loops. That number throws people off because they assume the bar is "Google hard" or "Meta hard." It is not. It is a different kind of hard. NVIDIA is not filtering for algorithmic breadth. They are filtering for systems depth, specifically hardware-aware systems thinking that most FAANG prep tracks do not even touch.

The onsite is typically three to four rounds: two coding rounds, one system design, one behavioral. Every round has hardware context underneath it, even when the surface question looks generic.

What the Coding Rounds Actually Test

The coding problems are C++, and the C++ matters. Not as a syntactic gatekeep, but because the questions hinge on language-level memory behavior.

One common pattern is a function that allocates on the heap, passes ownership around through raw pointers, and has a subtle double-free or leak hiding three levels deep. You are not just debugging. You are reasoning about object lifetime, move semantics, and what happens when that code runs across thousands of parallel threads.

A second pattern I have seen from others in the community: write a matrix multiply that is cache-friendly. Not just correct, but performant. The interviewer wants to know if you understand row-major vs. column-major access patterns and why that matters when your data does not fit in L2.

// Naive version -- cache-unfriendly column access
for (int i = 0; i < N; ++i)
  for (int j = 0; j < N; ++j)
    for (int k = 0; k < N; ++k)
      C[i][j] += A[i][k] * B[k][j]; // B[k][j] strides badly

// Transpose B first, or restructure loop order
// This is the conversation they want to have

If you freeze on questions like this because your prep was "time complexity and space complexity," that is the gap.

System Design: Not Cloud, Not Microservices

The system design round at NVIDIA is not about designing Twitter or a URL shortener. It is about distributed training infrastructure and GPU memory constraints.

The canonical question involves choosing between data parallelism and model parallelism for a large model that does not fit on a single GPU. The interviewer expects you to reason through:

Memory bandwidth limitations per GPU die
Communication overhead in an all-reduce operation vs. pipeline parallelism
When tensor parallelism makes sense vs. when it adds synchronization overhead that kills throughput

Candidates who walk in with a standard system design template (load balancers, databases, caches) get lost quickly. The vocabulary is different. The tradeoffs are hardware-bound, not infrastructure-bound.

What to Do Differently

If NVIDIA is on your target list:

Swap some LeetCode time for C++ systems reading. Specifically: Effective Modern C++, and then papers on GPU memory hierarchies. You do not need to be a CUDA engineer, but you need fluency in the concepts.

Practice talking about hardware tradeoffs out loud. When you solve a problem, add: "and here is why this matters on hardware with limited L2 cache" or "here is how this changes under a parallel write scenario." Make it a reflex.

Read NVIDIA engineering blog posts before your loop. They publish on NVLink, multi-GPU training, and memory optimization regularly. The vocabulary from those posts shows up in interviews.

Do not treat behavioral as filler. NVIDIA values first-principles reasoning even in behavioral rounds. "Why did you make that technical decision?" is a real question they ask.

Has anyone else gone through an NVIDIA loop recently? Curious whether the system design format has shifted toward inference infrastructure or stayed focused on training. Drop your experience in the thread.

Full discussion with more specifics on the NVIDIA loop structure is here: NVIDIA SWE Interview 2026 community thread

For a detailed breakdown of the full interview process and prep timeline: NVIDIA Interview Process Guide

Azure Data Engineering Interviews Score 11 Points Below Standard Roles: What 15,000 Live Sessions Show

Alex Bell — Wed, 24 Jun 2026 12:19:22 +0000

Data Engineering Interview Scores Reveal a Gap Candidates Are Not Preparing For

Candidates preparing for data engineering interviews often spend the most time on system design, distributed computing, and coding challenges. The score data tells a different story. Analysis of 15,000+ live data engineering sessions through Final Round AI's Interview Copilot, spanning 2023 through 2026, shows the largest gaps are in Snowflake foundational concepts and SQL clause distinctions, not architecture or algorithms.

Azure Data Engineers score 11.3 points below the general data engineering average. Snowflake table type questions average around 40.0 out of 100, the lowest-scoring technical category in the dataset. SQL WHERE versus HAVING questions average around 50.0, below the general data engineer average of 55.4. Meanwhile, complex transformation questions score around 75.0 and AWS data engineering challenge questions score around 72.5. The pattern is clear: candidates handle difficult conceptual problems better than they handle specific foundational questions about the tools they claim to know well.

This article breaks down what the data shows across five role categories and translates it into actionable preparation for anyone interviewing in data engineering roles in 2026.

The Azure Data Engineering Gap No One Talks About

The most notable finding in Final Round AI's Interview Copilot dataset is the Azure Data Engineer scoring gap. Across 525 sessions, Azure Data Engineers averaged 44.1 out of 100. The general Data Engineer role averaged 55.4 across 14,201 sessions. That is an 11.3-point gap, and it is consistent across the dataset, not a statistical outlier.

Why does this gap exist? Azure-specific data engineering interviews test a narrower, more exacting set of tool knowledge compared to general DE interviews. Interviewers at companies hiring for Azure stacks, including Microsoft itself, Accenture's Azure practice, Capgemini, and enterprise clients running Azure Synapse Analytics pipelines, expect candidates to have precise answers about Azure-native services. The questions are not conceptual. They test whether a candidate can explain the difference between Azure Data Factory and Azure Databricks in an orchestration context, how Azure Synapse Analytics handles dedicated versus serverless SQL pools, and when to use Azure Event Hubs over Azure Service Bus for streaming ingestion.

A second factor is certification. Many Azure DE job descriptions list AZ-900, DP-203, or DP-300 certifications as preferred or required. Interviewers at companies filling these roles often frame questions around the conceptual boundaries these certifications test. Candidates who learned Azure through project work alone, without formal certification exposure, often miss the precise vocabulary and boundary cases that certification prep covers.

The implication: Azure Data Engineer preparation requires a dedicated track, not just general DE interview preparation. Adding two to three weeks focused on Azure Data Factory pipelines, Azure Databricks cluster management, and Synapse pool configuration moves a candidate out of the 44.1 average range and into territory closer to the general DE benchmark.

Why Snowflake Table Types Trip Up So Many Candidates

Snowflake table type questions are the lowest-scoring technical category in Final Round AI's Interview Copilot dataset, averaging around 40.0 out of 100. This surprises many candidates because Snowflake is one of the most widely used data platforms in the industry. The problem is that candidates know how to use Snowflake but not how to explain its foundational concepts precisely.

Snowflake has four table types. Permanent tables are the default: they persist data with both Fail-safe and Time Travel protections, and they consume the most storage. Transient tables drop Fail-safe protection but keep Time Travel, making them suitable for intermediate data that does not need the full recovery guarantee. Temporary tables exist only for the duration of a session and are invisible to other users, making them appropriate for session-specific staging work. External tables reference data stored outside Snowflake, such as in an S3 bucket, and do not store data internally.

The distinction between permanent and transient tables is where most candidates lose points. The question "when would you use a transient table in a production pipeline?" requires knowing that transient tables reduce storage costs for staging layers that are routinely rebuilt, without sacrificing query performance. Candidates often conflate transient and temporary tables, or cannot articulate the Fail-safe versus Time Travel trade-off.

Contrast this with Snowflake production monitoring, specifically credit management. Candidates score much higher on these operational questions. They know how to set resource monitors, configure virtual warehouse auto-suspend, and query the QUERY_HISTORY view for cost attribution. The operational side of Snowflake is what candidates use daily, so they answer fluently. The foundational taxonomy, the four table types and their trade-offs, is what they skip in preparation because it feels like documentation rather than real-world knowledge. Interviewers test it precisely because it separates candidates with genuine platform depth from those who have only operated it.

SQL WHERE vs HAVING: The Question That Scores Lower Than Expected

SQL WHERE versus HAVING questions average around 50.0 in Final Round AI's Interview Copilot dataset. That score is lower than the general Data Engineer average of 55.4, which means a question covering a two-decade-old SQL distinction is still tripping up experienced candidates.

The distinction is straightforward. WHERE filters rows before GROUP BY runs. HAVING filters aggregated results after GROUP BY runs.

Consider this query:

SELECT department, AVG(salary) AS avg_salary
FROM employees
WHERE status = 'active'
GROUP BY department
HAVING AVG(salary) > 80000;

The WHERE clause removes inactive employees before grouping happens. The HAVING clause removes departments whose active-employee average falls below 80,000. Removing WHERE and replacing it with HAVING does not produce an equivalent result: HAVING cannot reference non-aggregated columns directly in most SQL engines, and the filter would run at the wrong stage of execution.

Where candidates lose points is in explaining why this matters for query performance. Filtering early with WHERE reduces the row set before aggregation, which means less work for the GROUP BY operation. Using HAVING to filter what WHERE could handle instead forces the database to aggregate a larger row set before discarding results. For large fact tables, this distinction has real query cost implications.

Interviewers test this because it reveals whether a candidate understands SQL execution order, not just syntax. A candidate who cannot explain the execution order distinction has a ceiling on how well they will write production-quality analytical queries.

Data Engineers vs Data Scientists: What the Score Gap Reveals

Final Round AI's Interview Copilot data shows Data Scientists averaging 57.8 across 4,676 sessions and Data Engineers averaging 55.4 across 14,201 sessions. The 2.4-point gap is consistent across the dataset and reflects a structural difference in how each role's interviews are conducted, not a difference in candidate preparation effort.

Data engineering interviews test specific tool knowledge with answers that are either correct or incorrect. The four Snowflake table types have four specific answers. Azure Data Factory has a specific architecture. A Spark shuffle partition configuration either produces optimal execution or it does not. There is less room for a candidate to build a compelling answer around a defensible architectural argument.

Data science interviews, by contrast, include more questions where conceptual reasoning earns partial credit. A candidate explaining why regularization prevents overfitting can demonstrate understanding at multiple levels of precision. A candidate explaining feature store architecture for real-time model serving can discuss trade-offs without memorizing a specific implementation. This structural difference explains why DS interviews produce slightly higher average scores even when candidate preparation quality is comparable.

For candidates transitioning from data science into data engineering, this gap has a direct implication. The skills that allow a data scientist to score well through explanation and reasoning do not transfer directly to DE interviews. Tool-specific precision is what matters. A data scientist moving into a DE role needs to build out their Snowflake, Spark, dbt, and cloud-native pipeline knowledge to the point where they can answer narrow factual questions accurately, not just discuss architecture thoughtfully.

Five Things to Add to Your Data Engineering Interview Prep

Final Round AI's Interview Copilot data points toward five specific areas that move candidate scores meaningfully. These are not general study recommendations. They are the categories where the data shows consistent underperformance.

Snowflake table types with trade-offs. Memorize the four types: permanent, transient, temporary, and external. Know when each is appropriate. Practice explaining the Fail-safe versus Time Travel distinction and the storage cost implications of each type. This one category is averaging 40.0 in the dataset. Bringing it to 65-70 requires roughly two to three hours of focused study.

ETL pipeline design patterns with failure handling. Interviewers test whether a candidate knows how to build pipelines that fail gracefully. Idempotency, retry logic, and dead-letter queue patterns are the core concepts. A candidate who can describe how they would handle a partial load failure in an Azure Data Factory pipeline, including how they would configure retry policies and where they would log failed records, scores substantially higher than one who only describes the happy path.

dbt transformation approaches. dbt is increasingly tested in senior DE interviews. The distinction between dbt models, seeds, snapshots, and sources matters. Knowing when to use incremental models versus full refreshes, and how to configure the incremental strategy for different warehouse types, is the level of detail that earns high scores.

AWS Glue versus Spark trade-offs. AWS data engineering challenge questions average around 72.5 in the dataset, higher than the general DE average. Candidates who can explain when AWS Glue's managed ETL is preferable to a self-managed EMR Spark cluster, and vice versa, score well. The key dimensions are job complexity, data volume, and operational overhead tolerance.

WHERE versus HAVING with GROUP BY execution order. Review SQL execution order: FROM, JOIN, WHERE, GROUP BY, HAVING, SELECT, ORDER BY. Practice writing queries where both clauses are used intentionally and be able to explain why each clause appears where it does and what the performance implication would be if they were swapped or misapplied.

The Full Report

The complete analysis with the role-by-role comparison chart, including breakdowns for Data Analyst (54.5 average, 4,466 sessions), ML Engineer (56.7 average, 1,861 sessions), and the Azure-specific deep dive, is in Final Round AI's full report at https://www.finalroundai.com/blog/data-engineering-interview-questions-live-session-data

The interview questions candidates score worst on are not the ones they prepare for. Real data from 816,000 sessions explains why.

Alex Bell — Tue, 23 Jun 2026 19:22:50 +0000

The interview questions candidates score worst on are not the ones they prepare for. Real data from 816,000 sessions explains why.

Every software engineer who has spent months preparing for technical interviews knows the routine. LeetCode problems in the morning. System design walkthroughs in the evenings. Mock interviews on weekends. By the time the actual interview arrives, candidates can reverse a linked list, design a distributed cache, and explain the tradeoffs of eventual consistency without hesitation.

Then the interviewer opens with: "Can you tell me a bit about why you want to work here today?"

The room goes quiet. Not because the candidate does not know the answer, but because they never practiced it. They assumed it was a throwaway question, a 30-second warm-up before the real interview began. They treated it like small talk.

That assumption turns out to be wrong, and it costs candidates more than any failed coding problem ever does. Real session data from Final Round AI, drawn from 816,927 records across 35,511 unique interview sessions conducted between October 2022 and September 2025, shows exactly where candidates lose points. The pattern is striking: the lowest-scoring questions are almost all behavioral openers. The questions candidates skip in prep are the questions that damage their scores the most.

The scoring gap no one talks about

When the data is sorted by average candidate score, the bottom of the list is dominated by questions that most interview prep advice barely covers.

"Can you tell me how you heard about this position?" averages 25.7 out of 100 across 49 recorded sessions. That is not a rounding error. Candidates are essentially blanking on a question that sounds like it should take fifteen seconds to answer.

"Why did you choose that major and school?" averages 38.5 out of 100 across 161 sessions. This is a common question in recruiting screens and early-round interviews, particularly for candidates earlier in their careers. Yet more than 160 recorded sessions show candidates averaging below 40 on it.

"Why are you interviewing here today?" averages 44.4 out of 100 across 91 sessions. And "Why do you think we should hire you?" averages 46.7 out of 100 across 42 sessions.

All four of these questions share something: they are open-ended, they require self-knowledge, and they reward structured multi-part answers rather than a single correct response. None of them appear on any LeetCode list. None of them require memorizing an algorithm. And yet, across thousands of real interviews, they produce some of the lowest scores in the entire dataset.

The surprise in this data is not that candidates struggle with hard questions. It is that they struggle most with questions designed to be easy. These questions have no hidden algorithmic complexity. They have no trick. They are simply asking the candidate to articulate something they should know about themselves. The scores suggest that most candidates cannot do this under pressure in a structured way, because they have never tried.

The role breakdown reveals a counterintuitive gap

Across 35,511 sessions, the average score by role shows meaningful differences that carry practical implications.

Product Managers score highest at 58.9 out of 100 across 2,303 sessions. Data Scientists average 58.6 across 3,360 sessions. Software Engineers average 55.0 across 10,700 sessions. Software Developers average 52.0 across 2,450 sessions.

The most counterintuitive finding: Senior Software Engineers average 52.4 out of 100 across 2,688 sessions. That puts them below regular Software Engineers at 55.0 and only marginally above Software Developers at 52.0.

This gap runs counter to the obvious assumption that more experienced candidates would perform better. There are a few likely reasons for it.

Senior candidates tend to have strong opinions and well-formed habits. That works well in technical discussions and system design. It creates problems when behavioral questions require them to adapt their communication style to what an interviewer is looking for rather than what they prefer to say. A senior engineer who has been working at one company for six years often struggles to explain their motivations in a way that resonates with a hiring team at a different company with different values and a different culture.

Senior candidates also tend to underprepare behavioral responses specifically. They have been through many interviews, they know their technical material cold, and they assume the softer parts of the interview will take care of themselves. The data suggests they do not.

Product Managers scoring highest likely reflects the nature of PM interview training. PM interview prep culture heavily emphasizes storytelling, structured answers, and behavioral frameworks like STAR (Situation, Task, Action, Result). That skill transfers directly to the kinds of questions where candidates in other roles score poorly. PMs practice saying "why I want this role" and "why I'm the right fit" repeatedly, because those questions are core to PM interviews. Engineers rarely do.

Three reasons behavioral openers score low

The pattern has structural causes beyond individual candidate preparation.

Candidates treat them as warm-up. Most interview prep advice, both formal and informal, focuses on technical questions, case studies, and behavioral questions about past performance ("Tell me about a time when..."). Questions like "Why do you want to work here?" get less than five minutes of preparation time because candidates do not believe they matter much. When the question arrives in an actual interview, it lands with the full weight of stakes but none of the preparation. Candidates improvise. Improvised answers to open-ended questions tend to be vague, which explains the scores.

Generic answers do not demonstrate fit. "I've heard great things about the company culture" or "I'm excited about your product roadmap" are answers that could apply to any employer. Interviewers evaluating these answers know when they are hearing something rehearsed and hollow. The score reflects that knowledge. A specific answer tied to the candidate's actual background, with named products or named challenges or named people, performs better. Most candidates do not prepare specific answers because specific preparation requires research, and candidates generally prioritize technical prep over company research.

Short answers to questions that reward length perform poorly. "Why should we hire you?" is not a yes/no question and it is not a one-sentence question. A strong answer has multiple parts: skills, specific examples, and a connection between what the candidate offers and what the role requires. Candidates who give 30-second answers to this question tend to leave out at least one of these components. Incomplete answers score lower, and because candidates do not treat this question as a high-stakes item, they do not invest the time to build a complete answer. The result is a score that looks like a candidate who was not paying attention, even when the candidate is otherwise technically strong.

What to do with this by role

Software Engineers (average: 55.0) should audit which question types are missing from their prep. Most SE candidates have a solid technical foundation but have never run a timed mock for "Why do you want to work here?" or "Why should we hire you?". Building one or two strong answers for motivation questions, practiced out loud rather than just thought through, closes most of the gap. The goal is not memorization. It is having a structure: one sentence on personal motivation, one sentence on the specific role, one sentence on the company specifically. That three-part structure takes 60 to 90 seconds to deliver and scores materially higher than an improvised paragraph.

Product Managers (average: 58.9) already outperform other roles on behavioral questions, but they are not immune. PM candidates who score in the lower range tend to do so on quantitative or technical questions where they do not have strong frameworks. The tradeoff is that their behavioral score is strong. For PM candidates, the priority is maintaining that strength while not neglecting any technical components that appear in specific company interview processes. The data shows PMs are doing something right on behavioral prep. The lesson is to be intentional about what that is, rather than assuming it will carry over from job to job.

Senior Software Engineers (average: 52.4, below regular SWEs) have the clearest improvement path. The gap is behavioral, not technical. Senior candidates should practice articulating transitions: why they are leaving their current role, what they are looking for specifically, and how this particular opportunity fits. Vague answers to these questions are what drag the score down. A senior candidate who can clearly explain "I want to move from infrastructure work into product-facing systems because..." and follow it with concrete evidence will outperform the average by a meaningful margin. The fix is not hard. It requires a couple hours of deliberate prep on questions senior candidates have avoided for years.

For all roles, one practice pattern helps more than most: run a full mock that starts with the behavioral opener rather than the technical section. Most candidates warm up by practicing coding or case questions, which means the first time they say "Why do you want to work here?" under any kind of pressure is in the actual interview. Running the warm-up questions first, out loud, with a timer, produces noticeably better real-interview performance.

Where the full data lives

The analysis above draws on a subset of the findings. Final Round AI's full breakdown, which includes charts showing the role-by-role score distribution and a deeper breakdown of the specific behavioral question categories where scores diverge, is at https://www.finalroundai.com/blog/interview-questions-candidates-struggle-with.

The underlying dataset covers sessions across hundreds of companies and multiple years, making the pattern reliable rather than a result of any single industry or time period. The core finding holds across cohorts: candidates score lowest where they prepare least, and they prepare least for the questions that feel too easy to merit practice.

Which Tech Job Role Has the Hardest Interviews? Data from 100,000+ Real Sessions

Alex Bell — Sat, 20 Jun 2026 03:13:17 +0000

Java Developer has the lowest average interview score. Product Manager has the highest. Most candidates assume the opposite.

Final Round AI analyzed 100,870 live interview sessions across 16 job roles. The role difficulty ranking does not match what most candidates expect.

The Role Difficulty Ranking

Hardest to easiest (average score out of 100, 500+ sessions each):

Java Developer: 52.8
Full Stack Developer: 53.3
Finance Manager: 53.3
Network Engineer: 53.7
Salesforce Developer: 53.9
Site Reliability Engineer: 53.9
Software Engineer: 54.3
Project Manager: 54.3
Data Analyst: 54.5
QA Engineer: 54.8
Business Analyst: 55.1
Security Engineer: 55.3
Data Engineer: 55.4
Machine Learning Engineer: 56.7
Data Scientist: 57.8
Product Manager: 59.0

Software Engineer has 20,955 sessions in the dataset — by far the largest sample. Java Developer has 5,248 sessions. Product Manager has 2,814.

Why Java Developer scores lower than Software Engineer

Java Developer (52.8) underperforms Software Engineer (54.3) despite Java being a language most experienced engineers know well. The gap comes from interview format, not skill level.

Java Developer interviews focus on language-specific depth that rarely comes up in daily work: garbage collection tuning, ClassLoader behavior, the Java Memory Model, concurrent programming patterns. Engineers who have written Java for years still struggle with whiteboard questions about happens-before relationships or volatile semantics. Daily work does not transfer to the interview format without deliberate prep.

Why Product Manager scores highest

PM interviews follow a known structure: product design, metrics, estimation, behavioral. The evaluation criteria is stable across companies and interviewers. A candidate who has drilled 20 product design cases scores predictably in the high 50s to low 60s. The format rewards preparation more directly than open-ended SWE rounds.

The company breakdown

At Meta, Product Manager averages 65.6 — the highest role-company combination in the entire dataset. Well above Meta SWE (54.9) and Meta DS (57.3).

At Amazon, the order flips: Data Scientist (63.0) > Business Analyst (62.1) > Product Manager (61.9) > Software Engineer (55.0).

What this means for your prep

Match your prep strategy to your role score distribution, not general advice. If you are targeting a Java Developer role, LeetCode prep alone will not close the gap. You need to specifically drill JVM internals and language-specific interview questions.

For SWE roles at Google, Amazon, and Meta, scores cluster between 54.9 and 55.9 regardless of company. Company-specific prep matters less than role-specific prep.

Full analysis with charts: https://www.finalroundai.com/blog/tech-job-role-interview-difficulty-ranking

How Interview Difficulty at Amazon, Google, Meta, and Apple Changed From 2023 to 2025

Alex Bell — Fri, 19 Jun 2026 17:28:35 +0000

Based on 43,067 live interview sessions captured through Final Round AI's Interview Copilot between 2023 and 2025, we analyzed how candidate performance shifted at Amazon, Google, Meta, and Apple. The direction was not always what you would expect.

Meta had the steepest difficulty increase in 2025

Meta's average candidate score dropped from 56.5 in 2024 to 50.8 in 2025, a 5.7-point decline, the largest year-over-year shift of any FAANG company in the dataset. The interviewing bar tightened further in 2025, particularly around technical depth and system design. Meta is the only company that declined in both periods measured.

Apple was the only FAANG company where scores improved

Apple moved in the opposite direction. Candidate scores climbed from 50.0 in 2023 to 56.0 in 2024, then to 58.7 in 2025, the highest average of any FAANG company in the most recent year. Apple saw 713 scored sessions in 2025 versus 3,745 in 2024, so the 2025 cohort likely skewed toward more experienced candidates.

Amazon and Google both got harder after a strong 2024

Amazon had its best year in 2024 at 58.5, the highest score for any FAANG company in any year in this dataset. That dropped to 55.2 in 2025. Google followed a similar pattern: 58.8 in 2023, 56.9 in 2024, 55.8 in 2025.

Security Engineers score lowest across FAANG

By role: Security Engineer (51.0 avg) and iOS Developer (50.9 avg) score lowest. Data Scientist (62.4) and Business Analyst (62.3) score highest. Software Engineer, the most common role with 5,145 sessions, averages 55.4.

The 11-point gap between Security Engineer and Data Scientist reflects differences in interview structure. Data Scientist interviews follow known problem types (statistics, ML, SQL, case studies). Security Engineer interviews at FAANG are open-ended, threat modeling scenarios with no single right answer.

What this means if you are targeting these companies

Meta: Over-prepare. Focus on depth over breadth. One well-structured answer showing systems thinking and clear ownership scores higher than three shallow stories.

Amazon: Build specific, detailed Leadership Principle stories. The data shows you need outcome ownership, not just process participation.

Apple: The improving trend is encouraging but Apple loops routinely run five or more rounds in a single day. Practice sustaining performance across multiple hours.

Security Engineer: Technical competence alone does not translate into interview performance. Practice articulating how you think about risk through structured storytelling.

Full analysis with branded charts: finalroundai.com/blog/faang-interview-difficulty-trends-2023-2025

Data from 43,067 aggregated, anonymized live interview sessions. No individual user data included.

Data from 44,000 Interview Practice Sessions Shows Which Tech Companies Are Actually the Hardest

Alex Bell — Wed, 17 Jun 2026 20:02:38 +0000

Most "hardest tech company interviews" rankings are based on Reddit opinions, Glassdoor reviews, or surveys of a few hundred people. Final Round AI published a different kind of analysis.

Final Round AI tracks interview practice sessions across every major tech company. They analyzed 44,808 sessions from January 2023 through May 2025, calculated average practice scores for each company, and ranked them by actual performance data.

The results challenge what most people assume.

Meta Has the Lowest Average Practice Score of Any FAANG Company

Meta candidates average 55.5 out of 100 across 3,220 sessions, the lowest of any traditional FAANG company in our dataset. Here is how the full ranking looks:

Salesforce: 50.7 (1,062 sessions)
Meta: 55.5 (3,220 sessions)
TikTok: 55.5 (770 sessions)
Stripe: 55.6 (236 sessions)
Apple: 56.3 (4,528 sessions)
Google: 56.8 (16,604 sessions)
Amazon: 57.5 (18,932 sessions)
Nvidia: 58.2 (560 sessions)
Netflix: 59.2 (280 sessions)

Amazon has the most practice sessions by far and the highest FAANG average score. The most feared interview in tech is not the hardest to score on in practice.

Meta Got 11 Points Harder From 2024 to 2025

The year-over-year shift is where things get interesting for anyone currently prepping:

Meta: 56.5 in 2024 → 50.8 in 2025 (steepest decline of any company)
Amazon: 58.5 in 2024 → 55.2 in 2025
Google: 56.9 in 2024 → 55.8 in 2025
Apple: 56.0 in 2024 → 58.7 in 2025 (only major company that improved)

If you are prepping for Meta using resources from two years ago, you are likely under-prepared for what interviewers are asking today.

Why Amazon Scores Higher Despite Being "Harder"

Amazon has 18,932 sessions, more than Google and Apple combined. The depth of dedicated Amazon prep content (books, courses, communities built around the 16 Leadership Principles) shifts the baseline score upward. More candidates prepare more thoroughly for Amazon than for any other company.

The practical implication: fear of Amazon is well-calibrated for format complexity. But structured STAR-method preparation for Amazon actually works, and the data shows it.

TikTok and Stripe Are As Hard As Meta, With Far Less Prep Content

TikTok (55.5 across 770 sessions) and Stripe (55.6 across 236 sessions) sit right alongside Meta in difficulty. Both receive a fraction of the dedicated prep attention that FAANG companies get. If you are targeting either company, treat the prep with the same depth as a FAANG interview, not as an easier alternative.

Methodology

All data is aggregated and anonymized across real practice sessions on Final Round AI. Scores are automated evaluations of practice responses across relevance, structure, specificity, and completeness. No individual user data is included. Companies with fewer than 100 sessions were excluded.

Full report with charts: finalroundai.com/blog/hardest-tech-company-interviews

Curious what patterns others have seen. Does Meta being harder than Google match your experience preparing for both?