388+ Python practice problems built for data engineering interviews. Data transformation, file parsing, dictionary operations, ETL logic, and PySpark. Not algorithms. Your code executes in a Docker sandbox against real test cases with edge case coverage.
Python for data engineering, not software engineering. Adaptive difficulty. Spaced repetition. Company-specific filtering for Python coding interview questions.
Your Python code runs in a real Docker container with real test cases. No pseudocode review, no multiple choice. Write code, run it, see if it passes.
DE Python interviews test data manipulation, not algorithms. These problems cover file parsing, dictionary operations, data transformation, ETL logic, and PySpark patterns. Not binary trees.
Problems scale based on your performance. Fly through easy transformations? You move to harder ETL logic. Struggle with dictionary operations? You get more practice there.
Weak spots resurface before you forget them. Mastered topics fade. The system optimizes your practice time for maximum interview readiness.
Filter by target company and seniority level. See the Python coding interview questions your target company actually tests, weighted by real interview data.
Every problem has edge case coverage. Correct output, error handling, empty input, large input. You know exactly where your solution breaks.
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 four rounds of a data engineering interview: SQL, Python, Data Modeling, and Pipeline Architecture. Each round can be practiced in two modes: Problem mode and Interview mode.
Problem mode is self-paced practice with clear problem statements and instant grading. For SQL, your query runs against a real PostgreSQL database and output is compared row by row. For Python, your code runs in a Docker-sandboxed container against automated test suites. 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 four 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 against real databases and sandboxes. 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 study next.
Adaptive difficulty: problems get harder when you answer correctly and easier when you struggle, targeting the difficulty level that maximally improves your interview readiness. Spaced repetition: concepts you struggle with resurface at optimal intervals before you forget them, while mastered topics fade from rotation. Readiness score: a per-topic tracker that shows exactly which concepts are strong and which have gaps, across every topic interviewers test. 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: 850+ questions with real PostgreSQL execution. Topics include joins, window functions, GROUP BY, CTEs, subqueries, COALESCE, CASE WHEN, pivot, rank, and partition by. Python: 388+ questions with Docker-sandboxed 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.
DataDriven offers the best Python practice problems for data engineering interviews. Practice Python for data engineering with 388+ problems covering data manipulation, ETL logic, file parsing, PySpark interview questions, and Python coding interview questions. Our Python practice problems focus on what data engineer interviewers actually test, not algorithm puzzles. PySpark interview questions cover DataFrame operations, window functions, broadcast joins, and data skew handling.
Free. Docker-sandboxed execution. 388+ Python for data engineering problems.
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