Weekly blog

We analyzed 638 SQL interview questions to see who gets tested on SQL, how hard it is, and which companies lean on it most — the most approachable technical topic, and a role-defining one.

We analyzed 997 ML interview questions across modeling, evaluation, and system design — the best-paid data specialty ($225K median) and one of the toughest, at 26% rated hard.

We analyzed 517 generative-AI and LLM questions — the newest, fastest-growing, and second-hardest topic in data interviews, concentrated at the AI labs and central to the AI Engineer role.

We analyzed 725 statistics, probability, and A/B testing questions. At 35% rated hard, it's the hardest topic in the data interview — and the clearest line between a data hire and a software one.

We analyzed 1,001 product sense and metrics questions. Only 10% are rated hard — the most approachable topic on paper, and the most exposing in practice. Here's who gets tested and how.

We broke down what a data engineer interview really tests: pipelines (46% of the loop) and SQL — not algorithms, and almost never machine learning. Plus pay and demand for the role.

We analyzed thousands of quant interview experiences. Jane Street tops the market at 55% rated hard — about 4× the average interview — yet 71% still rate it a positive experience.

The strategy case (McKinsey 67% rated hard) and the IT-services loop (Accenture 10%) are two different exams under one label — yet both are rated positive 60%+ of the time.

We compared what data candidates prepare for against what those roles pay. The most prepared-for roles are the most accessible, not the most lucrative — ML Engineer pays most and gets the least prep.

We mapped where data candidates concentrate their prep against how hard each company's interview is. NVIDIA leads demand; Databricks is the toughest popular target at 41% rated hard.

We analyzed 15,000+ compensation bands to see what data, analytics, and AI roles really pay in 2026. ML Engineer leads at a $225K median; here is the pay by role, level, and the AI premium.

We analyzed 913,000+ interview experiences to rank the hardest companies to interview at. Jane Street tops the list at 55% rated hard — yet the toughest loops are often the best-rated.

We broke down 7,976 real data interview questions by topic, difficulty, and format. Behavioral is the #1 topic, only 20% is hands-on coding, and each role sits a different exam.

OpenAI vs Google: we compared the questions, difficulty, and candidate experience. AI labs test 4.6× more Generative AI; Big Tech tests far more SQL, coding, and A/B testing.

Behavioral is the #1 most-tested topic in data interviews — 26% of every loop, and 46% for managers. We analyzed 7,976 questions to show how much it really counts.

A field guide to Big Tech Data Engineer interviews: the dominant SQL screen, the conceptual layer, and how company emphasis changes prep.

OpenAI and Anthropic both test ambiguity, but not in the same way. Learn what each loop is really screening for and how to prepare.

What SQL interviews emphasize: ETL quality, validation, tooling choices, and how SQL supports trustworthy analysis.

SQL shows up first and more often; Python matters more when interviews move from reporting to manipulation and code reasoning.

Why threshold-aware answers beat leaderboard logic in big tech ML engineer interviews.

A sharper look at FAANG Data Analyst interviews: join logic, metric correctness, and why data quality matters as much as SQL syntax.

A 2026 BI analyst roadmap built from real interview patterns: monthly SQL rollups, window functions, and the right prep order.

Learn what BI interviews really test: turning analysis into decisions for non-technical stakeholders with clear, credible answers.

The top software engineering coding questions skew toward async thinking and debugging—not hard algorithm drills. Prep in the order interviews reward.

Netflix Data Engineer interviews heavily test data quality, reliability, and recoverability. Here’s how to prepare for that bar.

See how Meta, Apple, Amazon, Netflix, and Google differ in data science interviews—and how to prep for each one.

A practical guide to recurring SQL interview questions: joins, aggregation, window functions, and data validation.

How to answer top data analyst behavioral questions with better judgment, clearer trade-offs, and stronger stakeholder communication.

What ML interviews emphasize heading into 2026: prioritization, evaluation tradeoffs, experiments, and system-quality judgment.

Amazon DS interviews split between hard NLP depth and core model validation. Here’s what that reveals and how to prepare.

BI analyst interviews are front-loaded toward workflow, reporting, and metric logic. Prep in the order interviewers actually test.

We analyzed 231,000+ study sessions from 200,000+ candidates (Jan–Jun 2026) to see which companies, roles, and seniority levels they prepare for. AI Engineer is now the #2 role and Nvidia the #1 target.

Two Silicon Valley tech recruiters spilled the beans on how to craft a tech resume that doesn't just pass the ATS but demands attention.

Master PostgreSQL from basics to advanced SQL. Learn SELECT queries, joins, window functions, subqueries, and more with practical examples. Perfect for interview prep and daily use.