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If this sounds like you...
You've applied to 20+ jobs and barely get past the recruiter screen
You know SQL and Spark, but interviewers say your answers are "not deep enough"
You freeze during system design rounds and can't structure your thoughts
You practice on LeetCode but data engineering interviews feel completely different
You get nervous explaining trade-offs and end up rambling without a clear answer
You don't need more content. You need better answers.
See Exactly Why Your Answer Gets Rejected
Paste any data engineering interview answer. Our AI tells you what's wrong, what's missing, and rewrites it to FAANG-level quality.
Interview Question
What is a window function in SQL? When would you use one instead of GROUP BY?
Your Answer
“A window function is like an aggregate function but it doesn't group the rows. It uses OVER clause. You can do things like ROW_NUMBER and RANK. GROUP BY collapses rows but window functions don't.”
What's Wrong
- • Too vague — doesn't explain the PARTITION BY mechanism
- • No real-world use case or example query
- • Misses frame specification (ROWS/RANGE)
What's Missing
- • Performance implications vs self-joins
- • Running totals, moving averages use case
- • When NOT to use window functions
FAANG-Level Improved Answer
“A window function performs a calculation across a set of rows that are related to the current row, defined by a PARTITION BY clause, without collapsing the result set. Unlike GROUP BY which reduces rows to one per group, window functions retain every row while adding the computed value as a new column.
For example, to rank salespeople by revenue within each region: RANK() OVER (PARTITION BY region ORDER BY revenue DESC). I use them for running totals, moving averages, and de-duplication via ROW_NUMBER. They also eliminate expensive self-joins. However, they can be memory-intensive on large partitions — in those cases I'd consider pre-aggregating in a staging layer.”
Fix Your Answers in 3 Steps
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Our AI evaluates your answer against what interviewers expect. It finds gaps, weak spots, and missing depth.
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Everything You Need to Clear Interviews
Three tools. One goal: help you stop getting rejected.
“Can't I just use ChatGPT?”
| ChatGPT | DataEngPrep | |
|---|---|---|
| Evaluates YOUR specific answer | ✕ | |
| Structured scoring (1-10 per criterion) | ✕ | |
| Calibrated to real interviewer expectations | ✕ | |
| Follow-up questions like a real interview | ✕ | |
| Company-specific question banks (97+) | ✕ | |
| Hire / No-Hire verdict with reasons | ✕ |
How engineers use DataEngPrep
Common preparation patterns we see across users — not individual testimonials.
Before
Failing system-design rounds despite knowing the tools. Answers list components but skip trade-offs.
After
After analyzing 5–10 of their own answers, they start including capacity numbers, failure modes, and cost trade-offs in every response.
Before
Solve SQL problems easily but get 'not deep enough' feedback from FAANG interviewers.
After
The Answer Analyzer surfaces missing optimization reasoning and edge cases. Answers move from 5/10 to 8+/10 over a week.
Before
3+ years of experience but interview answers read like mid-level. Resume buries the impact.
After
Resume Optimizer + mock interviews shift the framing toward ownership, scale, and cross-team impact.
Questions from 98+ companies including Amazon, Google, Databricks, Snowflake, Meta, Netflix, and more
Invest in Your Next Offer
If this helps you clear even one interview, it pays for itself 100x over.
- ✓ Unlock full improved answers
- ✓ See the FAANG-level version of your answer
- ✓ 5 complete analyses with expert rewrites
- ✓ Follow-up questions per analysis
- ✕ Mock interviews
- ✕ Resume optimizer
- ✓ Unlimited answer analyses
- ✓ Full improved answers every time
- ✓ AI Mock Interviews (100/day)
- ✓ Follow-up questions & scoring
- ✓ Progress tracking & streaks
- ✓ Cancel anytime
- ✓ Everything in Core
- ✓ Resume Optimizer (ATS score + rewrite)
- ✓ Company-specific prep (Amazon, Google, etc.)
- ✓ SQL Playground with AI feedback
- ✓ Priority support
- ✓ All future updates
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Common Questions
How is this different from LeetCode or ChatGPT?
LeetCode focuses on coding puzzles. ChatGPT gives generic answers. We focus exclusively on data engineering interviews — real questions from Amazon, Google, Databricks — and our AI evaluates your specific answer against what interviewers actually expect.
Will this actually help me clear interviews?
If you can identify what's wrong with your answers before the interview, you stop making those mistakes in the real thing. That's exactly what the Answer Analyzer does — it finds gaps, fixes weak spots, and shows you FAANG-level responses.
I already know SQL and Spark. Do I still need this?
Knowing concepts and explaining them clearly under interview pressure are two different skills. Most rejections happen not because you lack knowledge, but because your answers are incomplete, unstructured, or miss what interviewers are looking for.
What if I'm preparing for a specific company?
We have question banks from 97+ companies including Amazon, Google, Databricks, Snowflake, Meta, and Netflix. You can practice with questions that are actually asked at your target company.
Is there a free option?
Yes. After signing in you get 5 free answer analyses every day and can browse all 1,800+ questions. Free analyses show your score, strengths, and gaps. Upgrade to unlock the full FAANG-level improved answer rewrites.
How does the mock interview work?
Our AI simulates a real data engineering interview — it asks follow-up questions, probes your understanding, scores you 1-10 on each answer, and gives you a final hire / no-hire verdict with specific improvement areas.
Stop Guessing. Start Improving.
Every interview you fail costs you months. Every answer you fix brings you closer to the offer. Start now — it's free.