Interview questions
Preparing for a data engineering interview at Morgan Stanley? This page contains 12 real interview questions sourced from verified Morgan Stanley interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Morgan Stanley data engineering interviews typically focus on SQL, Spark/Big Data, and Behavioral. The interview bar skews toward harder problems (5 hard vs. 4 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.
What are your strengths and weaknesses?
What is your biggest failure, and what did you learn from it?
Identify the Unix command that lists files with specific permissions
Write pseudo code for an ETL pipeline using Python and Pandas
Design a relational data model for a sales database, incorporating normalization techniques
Given two tables, calculate the row count for different types of joins (inner, left, right, and full outer)
What motivates you to join Morgan Stanley?
Write a SQL query to calculate the highest salary in each department using a window function
Explain Spark's narrow vs. wide transformations and when to use each
Explain the configuration of a Spark cluster for optimal performance
Explain the difference between coalescing and repartitioning in Spark
How would you manage a disagreement within your team about an ETL pipeline design?
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