Interview questions
Preparing for a data engineering interview at Freecharge? This page contains 10 real interview questions sourced from verified Freecharge interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Freecharge data engineering interviews typically focus on Spark/Big Data, SQL, and Behavioral. The interview bar skews toward harder problems (5 hard vs. 1 easy), suggesting emphasis on depth and system-level thinking.
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 is the difference between cache() and persist() in Spark? When would you use each?
Can you explain the architecture of Apache Spark and its components?
Tell me about a time when you faced a challenging situation at work and how you handled it.
What is a window function? Explain with an example.
Prioritize Spark optimizations by impact and effort. Discuss partitioning strategy, caching policy, join selection, shuffle reduction, and when each becomes a scalability or cost bottleneck.
Explain the difference between batch and streaming data processing in Data Fusion.
What are the limitations of AWS Glue and Lambda?
Articulate the architectural decisions, scalability trade-offs, and cost implications of designing an AWS data platform. How would you justify glue vs. EMR, Redshift vs. Athena, and when would each choice become cost-prohibitive at scale?
What are some best practices for writing efficient SQL queries?
How would you design a scalable data ingestion pipeline?
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