Interview questions
Preparing for a data engineering interview at McKinsey? This page contains 25 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 SQL, System Design/Architecture, and Python/Coding. The interview bar skews toward harder problems (13 hard vs. 8 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?
Tell me about a time when you had to influence stakeholders to adopt a data-driven approach
Aptitude Questions - time and work problems
Basic logical or analytical puzzle
How do you balance technical priorities with business needs?
Convert a sorted array into a Binary Search Tree
Detect a loop in a singly linked list
Problem based on lists operations
Solve a regex problem
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
NoSQL Database - Cassandra fundamentals
SQL questions: Group By, Joins, Correlated Queries
Solve a running sum query
What are the differences between normalization and denormalization? When would you use a denormalized structure?
Apache Spark Fundamentals - discuss
How would you ensure the pipeline is scalable for larger datasets?
Solve 7-8 data processing questions using PySpark on F1 Racing Data
What trade-offs would you consider when choosing between batch processing and real-time streaming?
Describe how you would design a data catalog for managing metadata
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