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
Written by the DataEngPrep Team
Our editorial team consists of experienced data engineers who have worked at top tech companies and gone through hundreds of real interviews. Every article is reviewed for technical accuracy and practical relevance to help you prepare effectively.
Learn more about our team →Related Articles
Practice These Questions
Ace Your Interview with AI Coaching
1,800+ expert answers, AI mock interviews, and personalized feedback to get you hired.