Interview questions
Preparing for a data engineering interview at McKinsey? This page contains 12 real interview questions sourced from verified McKinsey interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
McKinsey data engineering interviews typically focus on System Design/Architecture, SQL, and Python/Coding. The interview bar skews toward harder problems (8 hard vs. 2 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.
How do you ensure effective communication between technical and non-technical teams?
Convert a sorted array into a Binary Search Tree
Problem based on lists operations
Explain the concept of window functions in SQL and provide an example
Given a CSV file with raw customer transactions, design an ETL pipeline that cleans data, aggregates total sales by region and product, and loads into target table
What are the differences between normalization and denormalization? When would you use a denormalized structure?
Describe how you would design a data catalog for managing metadata
Design a data model for a ridesharing app
Design a data warehouse for 7-11 or 24x7 stores
Explain how you would optimize a data lake architecture for performance and cost-efficiency
How would you design a data platform to handle real-time transaction data for a retail business?
How would you implement data governance and security in your design?
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