Practice the problems being asked by 920 companies in data engineering interviews
real questions
companies
interview reports
Write a CDC query that deduplicates staging events by keeping only the latest row per user, then finds users that are new or whose plan has changed vs. the dimension table.
user_events | |||
|---|---|---|---|
| user_id | plan_tier | event_ts | rn |
| u1 | free | 2026-03-22 08:01 | 2 |
| u5 | free | 2026-03-22 11:45 | 2 |
| u2 | pro | 2026-03-22 08:03 | 1 |
| u3 | team | 2026-03-22 10:00 | 1 |
| u5 | enterprise | 2026-03-22 12:00 | 1 |
| u1 | pro | 2026-03-22 09:15 | 1 |
| u4 | free | 2026-03-22 11:30 | 1 |
| u6 | free | 2026-03-22 13:15 | 1 |
| 8 rows | |||
The queries interviewers actually write on the whiteboard
The distributed compute questions that trip up senior candidates
The data transforms and pipeline logic interviewers focus on
The interview round that separates analysts from engineers
Design the systems that move data at scale
Prescribed difficulty
Every question optimizes your odds of success
Work the recurring patterns
The same handful of question shapes come back across loops, so they can be rehearsed
Defend your solution
Explaining your reasoning is just as important as building it
Focus on what your company asks
Focus your time on the problems that will actually come up
Every question is tagged to the companies that ask it. Stop guessing whether window functions or Delta Lake matters for Databricks. Filter to your target company and focus on exactly what shows up in their loops.
A correct query still loses the round if you can't say why it's correct. Every problem ships a written solution covering the reasoning, the tradeoffs, and the mistake most candidates make. When the variant lands in a real loop, you already know its shape.
LeetCode is built for software engineers. The questions that show up at Airbnb, Stripe, and Netflix's data engineering loops are different. Practice on the questions you'll hit in the loop.
The first time you sit in a timed interview is the worst time to discover you freeze under pressure. Mocks scoped to your target company's format mean the real thing feels like a repeat, not a surprise.
Every hour you spend preparing directly increases your chance of getting the offer. No grinding through problems that won't show up.
Data Engineer Median Compensation
The offer is worth preparing for correctly.
Focus
Define your target companies and level. DataDriven cuts the scope of your focus areas by up to 60%, stripping away the noisy things interviewers don’t ask.
Sharpen
Every challenge narrows in on the area that optimally improves your interview success rate, so every minute that you spend is impactful.
Practice
SQL, Python, data modeling, and pipeline design sit in one place, at the depth the loop demands. Write real code against real data. No round walks in cold.
Ready
A readiness score reads your standing on every topic interviewers raise. Green across the board means you can book the loop without hedging. No guessing.
Your day job does not prepare you for what they actually ask in the interview. Practice the real rounds. Find your gaps before the interviewer does. Free forever.
DataDriven is a free web application for data engineering interview preparation. It is not a generic coding platform. It is built exclusively for data engineering interviews.
DataDriven is the only platform that simulates all 4 rounds of a data engineering interview: SQL, Python, Data Modeling, and Pipeline Architecture. Each round can be practiced in 2 modes: Problem mode and Interview mode.
Problem mode lets you set the pace, with an unambiguous problem statement and an immediate verdict on your output. For SQL, your query hits a real database and the returned rows are compared against the expected result set. For Python, your code executes for real and its output is checked the same way. For Data Modeling, you build schemas on an interactive canvas with structural validation. For Pipeline Architecture, you design pipelines on an interactive canvas with component evaluation and cost estimation.
Interview mode simulates a real interview from start to finish. It has 4 phases. Phase 1 (Think): you receive a deliberately vague prompt and ask clarifying questions to an AI interviewer, who responds like a real hiring manager. Phase 2 (Code/Design): you write SQL, Python, or build a schema/pipeline on the interactive canvas. Your code executes for real. Phase 3 (Discuss): the AI interviewer asks follow-up questions about your solution, one question at a time. You respond, and it asks another. This continues for up to 8 exchanges. The interviewer probes edge cases, optimization, alternative approaches, and may introduce curveball requirements that change the problem mid-interview. Phase 4 (Verdict): you receive a hire/no-hire decision with specific feedback on what you did well, where your reasoning had gaps, and what to focus on next.
Adaptive difficulty: clean solves push the next problem up a level and stalls pull it back down, holding you at the difficulty that moves your readiness fastest. Spaced repetition: shaky concepts come back around at the interval that beats forgetting, while the ones you have down drop out of rotation. Readiness score: a per-topic tracker that shows exactly which concepts are strong and which have gaps, across every topic interviewers ask about. Company-specific filtering: filter questions by target company (Google, Amazon, Meta, Stripe, Databricks, and more) and seniority level (Junior through Staff), weighted by real interview frequency data. All features are 100% free with no trial, no credit card, and no paywall.
SQL: 929 questions with real SQL execution. Topics include joins, window functions, GROUP BY, CTEs, subqueries, COALESCE, CASE WHEN, pivot, rank, and partition by. Python: 415 questions with real code execution. Topics include data transformation, dictionary operations, file parsing, ETL logic, PySpark, error handling, and debugging. Data Modeling: interactive schema design canvas. Topics include star schema, snowflake schema, dimensional modeling, slowly changing dimensions, data vault, grain definition, and conformed dimensions. Pipeline Architecture: interactive pipeline design canvas. Topics include ETL vs ELT, batch vs streaming, Spark, Kafka, Airflow, dbt, storage architecture, fault tolerance, and incremental loading.
The queries interviewers actually write on the whiteboard. Appears in 95% of DE interviews.
The interview round that separates analysts from engineers. Appears in 65% of DE interviews.
The data transforms and pipeline logic interviewers focus on. Appears in 78% of DE interviews.
Design the systems that move data at scale. Appears in 52% of DE interviews.
Production work and interview performance are different skills. You do not fail on knowledge. You fail on structuring an answer under time pressure with unfamiliar tables and someone watching. Every challenge here is timed and live so you build the muscle of producing correct code when it counts.
Every session targets your weakest topic against the pattern mix your target company focuses on most heavily. You are not working through a generic top-100 list. You are closing the specific gaps that would cost you the offer, so every hour of prep counts.
That round cuts more senior candidates than any other, and most people just re-read the Kimball book and hope. You get a product scenario, build the schema from scratch, and get evaluated on your grain, dimensions, and SCD strategies before you are doing it live.
That loop never ends on its own. A readiness score per target company shows exactly which rounds you would pass today and which ones would cost you the offer. When you can see the gap closing, you stop guessing and start scheduling.
They do. Databricks leans hard on Spark internals, Meta on SQL windows, Stripe on idempotent pipelines. Your practice set is weighted to your target company's actual pattern distribution, not a one-size-fits-all set of canned problems.