Interview questions
Preparing for a data engineering interview at KPMG? This page contains 10 real interview questions sourced from verified KPMG interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
KPMG data engineering interviews typically focus on Spark/Big Data, SQL, and Behavioral. The interview bar skews toward harder problems (4 hard vs. 3 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.
Demonstrate the difference between DENSE_RANK() and RANK()
Explain the differences between Data Warehouse, Data Lake, and Delta Lake
Joins and window functions - INNER, LEFT, RIGHT, FULL OUTER, ROW_NUMBER(), RANK(), DENSE_RANK()
Why did you leave your previous job?
Find the minimum and maximum values in an array
Alternatives to the Medallion Architecture
Create a DataFrame with default column types
Explain job execution in Spark: stages, tasks, Catalyst Optimizer
Split a DataFrame such that even numbers appear in one column and odd numbers in another
Walkthrough Spark's architecture, focusing on driver, executors, and DAGs
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