Data Engineer Mock Interview
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.
The whole loop a mock should prepare you for
A data engineer loop is 5 to 7 touches, and candidates get cut at very different stages. A good mock rehearses the round where you are actually weak, not just the one you enjoy. Bar height is the share of candidates still in the running.
Recruiter screen
Background, motivation, level, logistics. A gate, not a test. Nearly everyone who applies and matches gets here.
Technical screen ✕
Live SQL and Python in a shared editor. The biggest single cut, before a human reads deeper. A mock of this round is the highest-leverage practice.
Onsite loop
SQL, Python, data modeling, pipeline design, behavioral. The panel weighs your trade-offs and how you argue them, not just a green test.
Debrief
Interviewers compare notes and set level. Consistency across rounds matters as much as any single strong one.
Offer
Level sets the band. The gap between a screen-out and an offer is mostly closed in the SQL and modeling rounds.
Where a modeling round gets decided
The data modeling round decides more senior loops than any other, and nothing about practicing alone tells you whether your schema survives contact with a reviewer. A mock has to argue with it. Here is a representative prompt and the difference between a pass and a rejection.
Design the schema for a subscription business. How do you track a customer who upgrades, downgrades, then churns?
States the grain first ("one row per subscription state change"), models the change history as an SCD Type 2 on the plan dimension with effective and expiry dates, and keeps revenue in an additive fact so MRR rolls up cleanly.
Defends the choice under pushback: explains why a mutable "current_plan" column would destroy the history the business needs, and joins facts on the surrogate key so past invoices keep the plan that was true at the time.
Stores customers.current_plan as a single column and overwrites it on every change, so the upgrade and downgrade are gone and churn analysis is impossible.
Never states the grain, so the interviewer cannot tell what one row means, and cannot answer "what was MRR last March" without the history the model threw away.
Why mock interviews change your outcome
Reading questions and hints is the first step. Implementing them under time pressure with feedback is the second.
Reading solutions does not substitute for active practice
Reviewing 500 SQL solutions does not transfer cleanly to writing one from a blank editor under time pressure. Mock interviews force active recall: write the query, run it, debug, iterate. Educational research consistently finds that retrieval practice produces stronger retention than passive review, often by a substantial margin.
Self-assessment tends to miss the actual weak areas
Candidates routinely overestimate SQL fluency and underestimate data modeling. 3 weeks of grinding window functions does not help when the actual gap is articulating SCD Type 2 tradeoffs out loud. A simulator that spans all 5 domains surfaces blind spots within the first session, with accuracy quantified by subtopic across the next 20 questions.
Interview conditions change the kind of errors that appear
Writing SQL at a desk with documentation open differs meaningfully from writing it in 25 minutes with an interviewer watching. Time pressure produces specific failure modes: dropped PARTITION BY clauses, missing NULL handling, untested joins. Timed mock sessions exercise these failure modes so they surface in practice rather than in the actual onsite.
Tight feedback loops accelerate improvement
Solo practice produces a write-check-move-on loop that misses subtler issues: an O(n^2) approach when O(n) was available, a LEFT JOIN that silently drops rows. The AI reviewer names the exact case and why it bites. Over dozens of sessions, that gap against marking your own work compounds.
5 domains, 1,000+ questions
A typical DE interview loop 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 5.
SQL
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%
Python
Data manipulation, ETL logic, file parsing, API handling, and pandas. Code executes in a sandbox with access to pandas, numpy, and the standard library. The grader evaluates correctness, edge case handling, and adherence to Pythonic patterns. Topic breakdown: Data manipulation 40%, ETL logic 25%, File parsing 15%, API handling 10%, Pandas 10%
Data Modeling
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%
Pipeline Architecture
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%
Spark
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%
How the mock interview simulator works
4 steps from picking a domain to working your weak spots.
- 01
Pick a domain and difficulty
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.
- 02
Code or discuss in real time
SQL and Python rounds open a full code editor where the code executes against the practice harness. Data modeling and pipeline rounds use a discussion format where the AI asks follow-up questions based on the candidate's answers. Timer optional.
- 03
Get AI feedback line by line
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.
- 04
Review weak spots and drill them
After each session, you get a breakdown by topic. The platform tracks your accuracy over time and surfaces the topics where you're weakest. You work the weak topics directly, before the real loop finds them for you.
4 scorecard signals candidates routinely miss
Conversations with DE interviewers across FAANG, fintech, and high-growth startups surface a consistent set of evaluation criteria that candidates tend to underweight.
Recognizing the shape of a problem beats recall
Most DE interview pools cycle through roughly 40 core question patterns dressed up as hundreds of variations. The candidate who recognizes that a 'find consecutive events' prompt is a gaps-and-islands problem can solve it in around 12 minutes. The candidate who brute-forces it runs out of time. The bank groups questions by pattern so candidates can build recognition rather than chasing variants.
Edge cases carry significant weight
A query that produces the right output on sample data leaves several questions open: does it handle NULLs, does it survive duplicate timestamps, does it return the right answer on an empty table. Interviewers at Google and Amazon allocate meaningful partial credit based on the count of edge cases a solution handles. The AI grader reports which cases passed and which broke, with the specific failing input.
Communication carries about half the weight
Internal scorecards at most large companies separate problem solving from communication and weight them comparably. Explaining the approach before coding, naming a tradeoff out loud, walking through the rationale for one design over another all contribute. The discussion-mode rounds in the simulator exercise this directly with follow-up questions that score the depth of the response, not just the initial answer.
Speed distinguishes seniority levels
A mid-level candidate may solve a medium SQL problem in 25 minutes. A senior candidate solves the same problem in 10 minutes and uses the remaining 15 to discuss optimization, edge cases, and scaling behavior. The simulator tracks solve time per question and per topic; topics where solve time runs long get flagged for another pass.
DataDriven vs alternatives
How the simulator compares to a human coach, LeetCode, and solo practice.
| 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 scoring + 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 |
Where the questions come from
Questions sourced from verified interview reports, authored with reference to internal scorecards, and difficulty-calibrated to real round time budgets.
Sourced from interview reports at 275+ companies
The question pool is built from interview reports submitted by candidates who interviewed at Meta, Google, Amazon, Netflix, Uber, Airbnb, Stripe, Snowflake, Databricks, and roughly 265 other companies. Questions target the most frequently observed patterns rather than offering a random selection; each question maps to a specific pattern documented in the underlying dataset.
Authored with reference to interviewer scorecards
The questions and scoring rubrics on the platform are written with input from data engineers who have conducted interviews at large tech companies, using the structure of internal scorecards as a reference. Hints, evaluation criteria, and follow-up prompts reflect how interviewers score candidates in practice rather than the more generic feedback most coding platforms provide.
Difficulty calibrated to interview-round time budgets
Questions are tagged Easy, Medium, and Hard based on the time and depth observed in real interview rounds rather than academic complexity. A Medium question targets 15 to 25 minutes of solve time, matching the per-question budget in a 45-minute coding round after accounting for intros and discussion. A Hard question targets the senior and staff bands.
A plan for the mocks, by how long you have
Mocks work best on a schedule, not at random. The sequencing that holds across the loop: SQL and Python to fluency first, then modeling, then design, then full mock loops and behavioral last. Pick the track that matches your runway.
2-week sprint+
SQL reflexes + 1 mock a day
Drill the highest-frequency SQL patterns, timed, 2 problems a day, plus 1 full mock. Do not start a new tool this close in. Prioritize the rounds that carry the most weight.
Modeling + a full mock loop
3 worked schemas defended out loud, DE-style Python on messy input, then a timed end-to-end mock with a hire/no-hire verdict. Rehearse 2 behavioral stories in first person.
8-week plan+
SQL and Python to fluency
Weeks 1-2. Rebuild both to reflex on real interview problems, timed, with hidden-test discipline. This is the base everything else sits on.
Data modeling, drawn and defended
Weeks 3-5. At least 10 data modeling problems, grain stated first, each trade-off defended against pushback. The round that decides most senior loops.
Pipeline design, out loud
Weeks 6-7. 15-plus design prompts practiced aloud: ingest to serve, failure modes, re-runnability, orchestration named. Lead the conversation and surface trade-offs unprompted.
Mocks and behavioral rehearsal
Week 8. 4 to 6 full mock loops out loud, weighted to your weakest domain, plus behavioral stories in first person with quantified impact. Taper, do not cram new material.
Frequently asked questions
What is a data engineer mock interview?+
How many questions does DataDriven have?+
Is DataDriven better than hiring a mock interview coach?+
Does the code execute, or is it pattern-matched?+
How is DataDriven different from LeetCode for data engineering?+
What level of experience is DataDriven designed for?+
Start a mock interview
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition
Related mock interview guides
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.