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Finance, Payments & Fintech

Morgan Stanley

Investment banking / wealth management · Data Engineering interview

SQL join-cardinality, pandas ETL, PySpark monitoring + unit tests, and a big-data ecosystem (Sqoop/Hive/HDFS) breadth check.

Cloud / platform

AWS scenarios; Spark/Hadoop (Sqoop, Hive, HDFS, MapReduce)

Reported difficulty

Not reported

SQLPythonPySparkETLAWS

Hiring process

Recruiter

Technical phone (algo/DS/SQL + testing)

Final loop (3–5)

Hiring manager/leader

Offer

Round-by-round breakdown

SQL

High confidence

Number of rows in inner / left / right / full outer join (join cardinality)
Window functions, aggregations, dedup

Python

High confidence

Pseudo-code a dummy ETL in Python with data structures + pandas: extract, cleanse, transform, load to CSV
Arrays & stacks

PySpark / Spark

High confidence

Spark monitoring & performance management
Explain and create unit test cases using SQL & PySpark

Big-data ecosystem

Medium confidence

Sqoop (ingestion), Hive, HDFS, MapReduce; NoSQL
SDLC, Agile, DevOps, CI/CD, Git; AWS scenarios

System & pipeline design prompts

Financial data ETL pipeline (Python/PySpark): correctness, testing, governance

Prep — high priority

SQL (join cardinality, window functions)
Python pandas ETL
PySpark (monitoring/perf, unit tests); big-data ecosystem (Sqoop/Hive/HDFS/MapReduce)

Prep — medium priority

Data modeling
AWS scenarios
Behavioral

Rejection patterns

Cannot reason about join cardinality
No testing/CI-CD awareness
Shaky PySpark/ecosystem
Weak ETL design

Reported experiences

Data Engineer

ReportedFirst-hand

Recruiter → technical (algo/DS/SQL + testing/deployment) → final loop. Real Qs: join row-count for inner/left/right/full; pandas pseudo-ETL to CSV; Spark monitoring/perf; unit tests with SQL & PySpark; Sqoop/Hive/HDFS/MapReduce; AWS scenarios; arrays & stacks.

Rehearse for Morgan Stanley

Answered questions on what Morgan Stanley actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences

Openingloading the content…

Finance, Payments & Fintech

Morgan Stanley

Investment banking / wealth management · Data Engineering interview

SQL join-cardinality, pandas ETL, PySpark monitoring + unit tests, and a big-data ecosystem (Sqoop/Hive/HDFS) breadth check.

Cloud / platform

AWS scenarios; Spark/Hadoop (Sqoop, Hive, HDFS, MapReduce)

Reported difficulty

Not reported

SQLPythonPySparkETLAWS

Hiring process

Recruiter

Technical phone (algo/DS/SQL + testing)

Final loop (3–5)

Hiring manager/leader

Offer

Round-by-round breakdown

SQL

High confidence

Number of rows in inner / left / right / full outer join (join cardinality)
Window functions, aggregations, dedup

Python

High confidence

Pseudo-code a dummy ETL in Python with data structures + pandas: extract, cleanse, transform, load to CSV
Arrays & stacks

PySpark / Spark

High confidence

Spark monitoring & performance management
Explain and create unit test cases using SQL & PySpark

Big-data ecosystem

Medium confidence

Sqoop (ingestion), Hive, HDFS, MapReduce; NoSQL
SDLC, Agile, DevOps, CI/CD, Git; AWS scenarios

System & pipeline design prompts

Financial data ETL pipeline (Python/PySpark): correctness, testing, governance

Prep — high priority

SQL (join cardinality, window functions)
Python pandas ETL
PySpark (monitoring/perf, unit tests); big-data ecosystem (Sqoop/Hive/HDFS/MapReduce)

Prep — medium priority

Data modeling
AWS scenarios
Behavioral

Rejection patterns

Cannot reason about join cardinality
No testing/CI-CD awareness
Shaky PySpark/ecosystem
Weak ETL design

Reported experiences

Data Engineer

ReportedFirst-hand

Rehearse for Morgan Stanley

Answered questions on what Morgan Stanley actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences