Interview questions
Preparing for a data engineering interview at Bitwise? This page contains 19 real interview questions sourced from verified Bitwise interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Bitwise data engineering interviews typically focus on Spark/Big Data, Behavioral, and SQL. There's a solid mix of fundamental and advanced questions, making it accessible for candidates at multiple experience levels.
Use the difficulty filters above to focus your preparation. For each question, attempt your own answer first, then compare with our expert solution. You can also practice these questions in our AI Mock Interview Coach for real-time feedback.
What strategies can you use to handle skewed data in Spark?
How do you handle late-arriving data in Spark Structured Streaming?
How do you handle data skewness in Spark?
Can you share a time you faced a significant challenge and how you overcame it?
What challenges did you encounter when scaling your project?
What motivates you to pursue a change in your career?
Why did you choose a particular data storage solution?
Explain Step Functions for orchestration of workflows.
Lambda vs. Glue: Discuss use cases for both services.
S3 Storage Options: Describe Standard, Intelligent-Tiering, and Glacier.
How did you ensure data quality and integrity?
Calculate the cumulative transaction amount for each month using a transaction table.
Find the 2nd highest salary for each department using the DENSE_RANK() function.
Predicted outputs for different join types using two sample tables with NULL values.
Why not use ROW_NUMBER() instead? Discuss pros and cons.
How do you manage memory allocation in Spark?
How do you optimize long-running PySpark scripts on EMR?
Write PySpark code to filter and count records.
Describe a data pipeline you built and optimized.
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