Interview questions
Preparing for a data engineering interview at Datametica? This page contains 7 real interview questions sourced from verified Datametica interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Datametica data engineering interviews typically focus on Spark/Big Data, and SQL. The interview bar skews toward harder problems (4 hard vs. 0 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.
Explain the differences between Repartition and Coalesce. When would you use each?
Explain Fact and Dimension Tables with examples.
Convert complex SQL (CTEs, window functions, subqueries) to production-grade PySpark. Discuss when to use spark.sql() vs. DataFrame API, and the implications for testability, partitioning, and execution predictability.
How do you drop columns with null values in PySpark?
Explain Delta Table features – Z-ordering and Time Travel.
Explain Spark Architecture – Driver, Executors, and Tasks.
Explain Spark's execution process – Job/Stage/Task creation.
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