Practice the exact SQL, Python, data modeling, pipeline architecture, and Spark questions that top tech companies ask. Write real code, get line-by-line AI feedback, and track your weak spots across all 5 interview domains.
A data engineer mock interview is a simulated technical interview that tests the same skills real interviewers evaluate at companies like Meta, Google, Amazon, and Uber. It covers five distinct domains: SQL query writing, Python data manipulation, data modeling design, pipeline architecture reasoning, and Spark optimization. Each domain uses a different interview format, and candidates who prepare for only one or two domains consistently underperform.
The SQL round gives you a schema, a business question, and a blank editor. You write the query. It runs against a real database with test data. The interviewer (or AI grader) checks whether your output is correct, whether your approach handles edge cases, and whether your query would perform well on a table with 100 million rows. The Python round works the same way: you get a data transformation task and write code that actually executes.
Data modeling and pipeline architecture rounds are discussion-based. The interviewer describes a business scenario (say, designing the data model for a ride-sharing app) and asks you to walk through your design decisions. They probe with follow-up questions: Why did you denormalize that table? How would you handle late-arriving data? What happens when the schema changes? These rounds test your depth of understanding and your ability to reason about tradeoffs under pressure.
DataDriven simulates all five of these formats. Coding rounds give you a browser-based editor with real code execution. Discussion rounds use an AI interviewer that generates follow-up questions based on your specific answers, not a scripted path. The AI grader evaluates your performance on the same criteria that real interviewers use: correctness, edge case coverage, performance awareness, code quality, and communication clarity.
You can read 500 SQL solutions and still freeze when a blank editor stares back at you. Mock interviews force active recall: you write the query, run it, see the result, fix the bugs. That loop builds muscle memory in a way that reading never does. Research on skill acquisition is clear on this: retrieval practice beats passive review by a factor of 3x for long-term retention.
Most candidates overestimate their SQL skills and underestimate data modeling. They spend 3 weeks grinding window function problems while their real weakness is explaining SCD Type 2 tradeoffs. A mock interview simulator that covers all 5 domains exposes blind spots in the first session. DataDriven's topic tracking quantifies this: after 20 questions, you get an accuracy breakdown by domain and subtopic.
Writing SQL at your desk with Google open is different from writing SQL in 25 minutes with someone watching. Time pressure causes different errors. Candidates drop PARTITION BY clauses they'd never forget in a calm environment. Running timed mock sessions on DataDriven builds tolerance for that pressure so it doesn't spike on the day that matters.
If you practice alone, you write a query, check the output, and move on. You never learn that your approach was O(n^2) when O(n) was possible, or that your LEFT JOIN drops rows in a way you didn't notice. DataDriven's AI grader catches these issues and explains them. It's the difference between shooting basketballs in the dark and having a coach who tells you your elbow is too low.
Data engineering interviews test a wider range of skills than software engineering interviews. A typical DE interview loop at a FAANG company includes 2 coding rounds (SQL and Python), 1 data modeling round, and 1 system design round. Some companies add a Spark-specific round. DataDriven covers all five.
Window functions, CTEs, joins, aggregations, and subqueries executed against a real database. The AI checks correctness, edge case coverage, and query performance. Every question runs your actual SQL, not multiple choice.
Topic breakdown: Window functions 34%, Joins 28%, Aggregation 22%, CTEs 10%, Subqueries 6%
Data manipulation, ETL logic, file parsing, API handling, and pandas. Your code actually runs with access to pandas, numpy, and the standard library. The grader evaluates correctness, edge case handling, and whether your code follows Pythonic patterns.
Topic breakdown: Data manipulation 40%, ETL logic 25%, File parsing 15%, API handling 10%, Pandas 10%
Star schema design, fact vs dimension tables, slowly changing dimensions, data vault, and medallion architecture. The AI interviewer asks follow-up questions based on your answers, exactly like a real discussion round.
Topic breakdown: Star schema 30%, SCDs 20%, Normalization 20%, Data vault 15%, Medallion 15%
Batch vs streaming, idempotency, orchestration, failure handling, and schema evolution. System design rounds where the AI probes your reasoning with follow-up questions that get harder as you answer correctly.
Topic breakdown: Batch vs stream 25%, Reliability 25%, Orchestration 20%, Schema evolution 15%, Scaling 15%
Partitioning strategy, shuffle optimization, broadcast joins, UDFs, and Spark SQL. Questions target the performance tuning and debugging that interviewers care about, not just API syntax.
Topic breakdown: Partitioning 30%, Joins 25%, Performance 20%, SQL 15%, Streaming 10%
Choose SQL, Python, Data Modeling, Pipeline Architecture, or Spark. Set your target difficulty from entry-level to staff engineer. The simulator selects questions matching your profile.
SQL and Python rounds give you a full code editor where your code actually runs. Data modeling and pipeline rounds use a discussion format where the AI asks follow-up questions based on your answers. Timer optional.
The grader checks correctness, edge cases, performance, code style, and communication. For SQL and Python, your code runs and you get instant feedback on what passed and what broke. For discussion rounds, it evaluates the completeness and depth of your reasoning.
After each session, you get a breakdown by topic. The platform tracks your accuracy over time and surfaces the topics where you're weakest. Targeted drills fill the gaps before your real interview.
We interviewed 45 data engineering interviewers at companies across FAANG, fintech, and high-growth startups. Four patterns came up repeatedly.
A senior data engineer at Meta told us their team recycles about 40 core patterns across hundreds of question variations. The candidate who recognizes that a "find consecutive events" question is really a gaps-and-islands problem finishes in 12 minutes. The candidate who brute-forces it runs out of time. DataDriven groups questions by pattern so you build this recognition muscle.
Your query returns the right answer on sample data. Great. But does it handle NULLs? What happens when two events share the same timestamp? What if the table is empty? Interviewers at Google and Amazon give partial credit based on how many edge cases your solution handles. DataDriven's AI grader catches the ones you miss and tells you exactly what broke.
At most companies, the interviewer fills out a rubric after the session. "Problem solving" is one axis. "Communication" is another, weighted equally. Can you explain your approach before coding? Can you walk through a tradeoff? DataDriven's discussion rounds train this skill directly: the AI asks "why did you choose X over Y?" and scores the depth of your answer.
A mid-level candidate solves a medium SQL problem in 25 minutes. A senior candidate solves the same problem in 10 and spends 15 minutes discussing optimization, edge cases, and how the query would perform at scale. DataDriven tracks your solve time per question and per topic. If your window function questions average 30 minutes, you know where to drill.
| Feature | DataDriven | Coach ($150/hr) | LeetCode | Solo Practice |
|---|---|---|---|---|
| Data engineering questions | 1,000+ across 5 domains | Varies by coach | < 50 DE-specific | You find them yourself |
| Real code execution | Real SQL + Python execution | Depends on setup | Generic SQL engine | Local setup required |
| AI grading + feedback | Line-by-line, every question | Human review (limited time) | Pass/fail only | None |
| Discussion rounds | AI follow-up questions | Yes (best format) | Not available | Not possible |
| Cost | Free tier + premium | $100-200/hour | $35/month | Free |
| Availability | 24/7, unlimited sessions | Scheduling required | 24/7 | 24/7 |
| Tracks weak spots | Automatic topic tracking | Manual notes | Basic stats | Spreadsheet if disciplined |
Our content team analyzed interview questions reported from Meta, Google, Amazon, Netflix, Uber, Airbnb, Stripe, Snowflake, Databricks, and 265+ other companies. We identified the most frequently tested patterns and built questions that target them directly. This isn't a random collection. Each question maps to a specific pattern that appears in real DE interviews.
Every question is written by a data engineer who has conducted interviews at a top tech company. They know what the rubric looks like. They know which mistakes interviewers penalize and which ones they forgive. That context shapes every question, hint, and grading criterion on the platform.
Our questions are tagged Easy, Medium, and Hard based on actual interview difficulty, not academic complexity. A "Medium" question on DataDriven takes 15 to 25 minutes, matching the time allocation in a real 45-minute coding round (accounting for intro and questions at the end). A "Hard" question pushes senior and staff candidates.
DataDriven's grader doesn't just check whether your output matches an expected result. It evaluates your solution on multiple dimensions, the same way a human interviewer would.
For SQL questions, the grader checks your query against edge cases: NULL values, empty tables, duplicate timestamps, and extreme values. If your query returns correct results on the simple case but fails on NULLs, you see exactly what broke and why.
Beyond correctness, the grader evaluates query structure. It checks whether you used a window function where a self-join would be less efficient. It flags unnecessary subqueries that could be CTEs. It identifies missing indexes that would matter at production scale. This isn't generic code review. It's feedback calibrated to what DE interviewers actually care about.
For Python questions, your code actually runs with access to pandas, numpy, and standard library modules. The grader checks output correctness, but it also evaluates code quality: Are you using list comprehensions where appropriate? Is your error handling adequate? Does your solution scale linearly or does it have hidden O(n^2) loops? These are the distinctions that separate a "hire" from a "strong hire" on a real rubric.
For discussion rounds (data modeling and pipeline architecture), the AI interviewer generates follow-up questions based on your specific answers. If you propose a star schema, it asks about the grain of your fact table. If you suggest daily batch processing, it asks what happens when a file arrives late. The grader evaluates the completeness and depth of your reasoning across the full conversation, not just your initial answer.
DataDriven serves three groups of people, each with different starting points and goals.
Career switchers moving into data engineering from analytics, backend engineering, or data science. You know some SQL but haven't been tested on window function edge cases or multi-CTE patterns. You've written Python scripts but never built an ETL pipeline. DataDriven's progression system starts you at fundamentals and ramps to interview difficulty over 4 to 6 weeks.
Working data engineers preparing for a specific interview. You have the skills but haven't interviewed in 2 years. Your SQL is strong on daily work queries but rusty on interview patterns. Your data modeling instincts are good but you haven't articulated them out loud under time pressure. DataDriven's mock interview mode lets you simulate full interview rounds at your target company's difficulty level.
Senior and staff engineers targeting top-tier companies. You can solve any SQL problem but need to practice doing it in 10 minutes, not 25. Your system design knowledge is deep but you need to practice structuring verbal explanations. DataDriven's hard and expert difficulty tiers, combined with the AI discussion mode, target this preparation gap.
A simulated technical interview that tests the same skills real DE interviewers evaluate: SQL query writing, Python data manipulation, data modeling design, pipeline architecture reasoning, and Spark optimization. DataDriven's mock interviews use real code execution and AI grading to give you feedback identical in structure to what a human interviewer would provide.
Over 1,000 questions across 5 domains: SQL (400+), Python (250+), Data Modeling (200+), Pipeline Architecture (150+), and Spark (75+). Questions are sourced from interviews at 275+ companies and authored by engineers from Meta, Google, Amazon, Netflix, and Uber.
A human coach costs $100 to $200 per hour and requires scheduling. DataDriven is available 24/7 with unlimited sessions. For coding rounds (SQL and Python), the AI grader provides line-by-line feedback comparable to what a coach offers. For discussion rounds (data modeling and pipeline architecture), a human coach has a slight edge on nuance, but DataDriven's AI follow-up questioning is close. Most candidates use DataDriven for daily practice and hire a coach for 1 to 2 final prep sessions.
Your code runs for real. SQL executes against a production-grade database. Python runs with access to pandas, numpy, and standard library modules. The AI grader evaluates the actual output of your code, not a pattern match against expected syntax.
LeetCode focuses on algorithm problems for software engineers. It has fewer than 50 questions relevant to data engineering and no coverage of data modeling, pipeline architecture, or Spark. DataDriven is built specifically for DE interviews with 1,000+ questions, real code execution, and AI grading that evaluates DE-specific criteria like query performance, edge case handling, and schema design rationale.
Entry-level through staff engineer. Questions are tagged by difficulty, and the mock interview simulator adjusts based on your target level. Junior candidates focus on SQL fundamentals and Python basics. Senior candidates get complex multi-CTE queries, system design scenarios, and data modeling tradeoff discussions.
No signup required for your first session. Pick a domain, write real code, get AI feedback.
Deep dive into SQL interview patterns, window functions, CTEs, and real SQL execution practice.
Practice Python data manipulation, ETL logic, and file parsing with real code execution.
Star schema design, SCDs, and data vault with AI discussion-mode follow-up questions.