Python for Data Engineering: Interview Questions & Answers
Essential Python interview questions for data engineers covering PySpark, pandas, file handling, API design, and ETL scripting patterns.
Key Takeaways
- ✓Python in Data Engineering Interviews
- ✓PySpark Patterns
- ✓Common Coding Challenges
Python in Data Engineering Interviews
Python is the lingua franca of data engineering. While SQL tests your data manipulation skills, Python tests your ability to build, automate, and orchestrate.
Common Python topics in DE interviews:
- File handling (CSV, Parquet, JSON)
- API consumption and REST client design
- PySpark DataFrame operations
- pandas for data transformation
- Error handling and logging patterns
- Unit testing data pipelines
PySpark Patterns
Most Spark jobs in production are PySpark. Key patterns to know:
- Reading/writing Parquet and Delta files
- UDFs vs built-in functions (and why to avoid UDFs)
- Broadcast variables and accumulators
- Schema enforcement and evolution
- Testing PySpark code with local SparkSession
Common Coding Challenges
Unlike SWE interviews, DE coding rounds focus on data manipulation:
- Parse a nested JSON file and flatten it
- Implement a simple ETL pipeline with error handling
- Write a deduplication function
- Build a data validation framework
- Implement retry logic with exponential backoff
Reviewed by Aditya Kumar · DataEngPrep Editorial Team
Drafted by the editorial team and signed off by Aditya Kumar, founder and lead editor at DataEngPrep. Questions are sourced from real interviews, initial answers are drafted with AI assistance, and every section is human-edited for technical accuracy, relevance to current FAANG hiring rubrics, and clarity. Articles are reviewed periodically as interview patterns evolve.
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