I have been on both sides of over 250 FAANG data engineering interview loops. As a candidate, I did about 20 loops in a single job search. As an interviewer, I have watched hundreds of candidates walk in prepared for the wrong things.
The data engineering interview prep market in 2026 charges $5 to $15 a month for platforms that cover maybe two of the four rounds you will actually face. You end up stitching together three or four subscriptions, paying $50+ a month, and still walking into your onsite with a blind spot that gets you eliminated.
I built DataDriven.io to cover every round in one place. It is free. Every feature, every problem, every company tag. No trial, no credit card, no paywall. Here is what that actually includes and why each piece matters.
Real Code Execution, Not Multiple Choice
The single most important thing about interview prep is that it has to match the format of the interview. You will not get multiple choice questions in a DE onsite. You will get a blank editor and a prompt.
DataDriven runs your SQL and Python in real execution environments. You write a query against actual tables, it executes, you see output, and you find out whether your logic holds up against edge cases. This is not "select the correct answer from four options." This is the same pressure you will feel in the interview: a blank screen, a problem, and a clock.
Every problem is tagged by company and weighted toward what those companies actually ask. A Meta SQL round looks different from a Databricks SQL round. Practicing generic problems without knowing what your target company emphasizes is prep with a blindfold on.
AI Mock Interviews That Simulate the Full Loop
The hardest thing to practice alone is the conversational pressure of a live interview. Solving a problem in silence is a fundamentally different skill than solving it while someone asks you to explain your approach, challenges your assumptions, and throws follow-ups at you.
DataDriven's AI mock interviews simulate real technical and behavioral rounds. The AI asks follow-up questions based on your responses, evaluates your communication and technical depth, and gives you multi-dimensional feedback on where you lost clarity or missed an opportunity to demonstrate deeper understanding.
This matters because the interview is not just about getting the right answer. I have watched candidates solve problems correctly and still get rejected because they could not articulate why they made the choices they did. The mock interview is where you build that muscle.
The Right Data Modeling Practice
55% of DE interview loops include a data modeling round. It is the round with the highest elimination rate because almost nobody practices it. The reason is simple: automating evaluation of schema design is genuinely hard. Most platforms skip it entirely.
DataDriven does not skip it. You get a business scenario, you design a schema from scratch using an interactive schema designer, and you get evaluated on grain, dimensions, normalization trade-offs, and SCD strategies. This is the round that kills experienced engineers who have been writing pipelines for years but have never had to defend a schema design under time pressure.
If you are prepping for interviews and not practicing data modeling, you are leaving the highest-leverage round completely to chance.
Structured Courses Built Around Interviews, Not Textbooks
There is a difference between learning data engineering concepts and learning how those concepts get tested in interviews. A course that teaches you what a star schema is does not help you when the interviewer says "design the data model for a ride-sharing marketplace and defend your grain choices."
DataDriven's courses are structured around interview patterns, not academic curricula. SQL, Python, data modeling, pipeline architecture, and Spark internals, all framed as "here is how this gets asked, here is what the interviewer is evaluating, here is what a strong answer looks like versus a weak one." The content covers the same topics you would find in a $200 course, except it is built by someone who has actually conducted hundreds of these interviews and knows what separates a hire from a no-hire.
Adaptive Difficulty That Targets Your Weak Spots
Solving 500 random problems feels productive. It is not. If your window functions are solid but your self-joins fall apart under time pressure, doing 50 more window function problems is wasted effort.
DataDriven tracks your performance by round and by topic, identifies where you are weakest, and feeds you more of that. It also adjusts by company, because a Netflix prep track and an Amazon prep track emphasize different things. The readiness score tells you, per round, which ones you would pass today and which ones would cost you the offer.
This is the information most candidates do not have until after they get rejected. Getting it before the interview is the difference between focused prep and aimless grinding.
Why All of This Is Free
The marginal cost of one more user on DataDriven is close to zero. The execution environments are containerized and ephemeral. Storage costs pennies. The expensive part was building it, not running it.
I am a staff-level data engineer with a day job. I built this because the data engineering community gave me my career through free blog posts, open source tools, and people answering questions on Reddit at midnight. Charging $10 a month for prep that costs me almost nothing to serve, to people who are often between jobs, felt wrong.
DataDriven.io. No account required to start. Open it, pick a challenge, and find out which round would cost you the offer before it actually does.
Do the DataDriven 75 to prepare for your upcoming data engineering interview
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