Real interview questions asked at McKinsey. Practice the most frequently asked questions and land your next role.
McKinsey data engineering interviews test your ability across multiple domains. These questions are sourced from real McKinsey interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward senior-level depth (13 of 25 are tagged hard). Recurring themes are partition, join, and spark — these patterns appear most often in real interviews and reward the deepest preparation. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 25 curated questions: 8 easy, 4 medium, and 13 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (14), join (10), spark (9), optimization (8), sql (5), and window (4). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
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
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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