Interview questions
Preparing for a data engineering interview at KPMG? This page contains 21 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, General/Other, and SQL. The interview bar skews toward harder problems (10 hard vs. 7 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()
If you already have an offer, why are you exploring other roles?
Introduce yourself, highlighting key projects and tech stacks
Why did you leave your previous job?
Are you willing to relocate to Bangalore?
Count occurrences of a specific word in a file
Discuss Logical Plan vs Physical Plan
Discuss the nature and volume of data you manage daily
Explain your day-to-day responsibilities as a Data Engineer
Match countries in a pairwise format
Find the minimum and maximum values in an array
Count occurrences of each character in a string
Alternatives to the Medallion Architecture
Compare ORC and Parquet
Create a DataFrame with default column types
Explain job execution in Spark: stages, tasks, Catalyst Optimizer
Justify the choice of your current tech stack. Why Spark, Hadoop, or cloud platforms?
Split a DataFrame such that even numbers appear in one column and odd numbers in another
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