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Data engineering interview · Finance, Payments & Fintech

Morgan Stanley

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

Stages: 54 with reported questions
Difficulty: Not rated
Cloud: AWS scenarios
Evidence: 2 of 2sources are first-hand

What Morgan Stanley does

And what its data engineers work with.

Business: Investment banking / wealth management
Platform: AWS scenarios; Spark/Hadoop (Sqoop, Hive, HDFS, MapReduce)
Stack named in its interviews: SQL
Python
PySpark
ETL
AWS

The loop

Stages in the order candidates meet them.

01Recruiter
02Technical phone (algo/DS/SQL + testing)
03Final loop (3–5)
04Hiring manager/leader
05Offer

5 stages. 4 rounds have reported questions, described below.

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
SQL
High confidence
- SQL
2 reported questions. See them below
ROUND 02
Python
High confidence
- Python
2 reported questions. See them below
ROUND 03
PySpark / Spark
High confidence
- SQL
- Spark
2 reported questions. See them below
ROUND 04
Big-data ecosystem
Medium confidence
- Spark
- Cloud and tools
2 reported questions. See them below

Reported interview questions

By round. Questions marked Expected are written from the company's standards.

Reported from accounts and guides, by round

SQLHigh confidence

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

PythonHigh confidence

Pseudo-code a dummy ETL in Python with data structures + pandas: extract, cleanse, transform, load to CSVPractise Data Pipeline
Arrays & stacksPractise Python

PySpark / SparkHigh confidence

Spark monitoring & performance managementPractise Apache Spark
Explain and create unit test cases using SQL & PySparkPractise SQL

Big-data ecosystemMedium confidence

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

Expected written from Morgan Stanley's standards

Nobody reported these exact questions at Morgan Stanley. They are the questions a loop for each of its top topics is known to ask, so prepare for them as likely, not as confirmed.

Return the top 3 rows per group, and say what happens on ties.SQL
Find the latest row per key in a table that has duplicates.SQL
What is a shuffle, and how would you make a job shuffle less?Spark
One hot key makes a join slow. What do you do?Spark
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python
When would you choose Airflow, and when would you not?Cloud and tools
How do you load files from object storage into a warehouse?Cloud and tools
Tell me about a pipeline you owned that failed in production.Behavioral
Describe a disagreement about a design, and how it ended.Behavioral

Must know

Ranked by how much of the evidence points at each topic.

SQLReported3 reported questions across 2 rounds
Spark and big dataReported2 reported questions across 2 rounds
Python and codingReported1 reported question across 1 round
Cloud and toolsReported1 reported question across 1 round
Behavioral and communicationIn the prioritiesNamed in the prep priorities
Data modelingIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

SQL (join cardinality, window functions)
Do first (3)
Python pandas ETL
PySpark (monitoring/perf, unit tests); big-data ecosystem (Sqoop/Hive/HDFS/MapReduce)
Data modeling
Then
AWS scenarios
Behavioral

Rejection patterns

Why candidates were turned down, as reported.

Cannot reason about join cardinality
SQL is named in 37 of 50 company accounts.
No testing/CI-CD awareness
Shaky PySpark/ecosystem
Spark is named in 25 of 50 company accounts.
Weak ETL design
Design is named in 27 of 50 company accounts.

Reported experiences

5 reported. Each opens and closes. Composites are labelled.

5 accounts

Data EngineerDate not statedReportedCandidate account
A recruiter call, a technical round (algorithms, data structures, SQL, testing and deployment), then a final loop. Questions included row counts for inner, left, right and full joins, writing a small ETL in pandas to a CSV, Spark monitoring and tuning, unit tests for SQL and PySpark, Hadoop tools, AWS scenarios and arrays and stacks.
What to take from it Know how to test your pipelines, not just write them.
Data EngineerIndia · 2021GeeksforGeeks
Five rounds. The first was an online HackerRank test in four timed parts: SQL coding, multiple choice on Python, SQL and Unix, a data structures problem and database administration scenarios. The author says window functions, joins and database ideas matter most.
What to take from it Prepare for a mixed online test, with a clock.
Data EngineerMumbai, India · Mar 2026Glassdoor
The first round was an online test with two SQL questions and one Python question. The SQL tasks were removing duplicate rows and finding students absent three days in a row. This was in Mumbai in March 2026.
What to take from it Practise duplicates and consecutive-days problems.
Questions named
- Delete duplicate rows
- Students absent for three consecutive days
Data Engineer2022 to 2024Glassdoor
A recruiter call about basic coding, then a spoken coding test in Python, SQL and Scala. Another candidate had a phone call with whiteboard coding and questions on unit and integration tests, deploying to production and finding errors.
What to take from it Be ready to talk about testing and deployment.
Data Engineer2025Glassdoor
A 30-minute interview that ended after 15 minutes. The candidate had a bad experience.
What to take from it Some interviews are short and unclear. Stay calm and ask for feedback.

Sources and how sure we are

Good first-hand signal; testing/CI-CD awareness and join-cardinality reasoning stand out.

GeeksforGeeks — Morgan Stanley Data Engineer 2021
otakutechie — Morgan Stanley Data Engineer 2025 (Round One)

Rehearse the Morgan Stanley loop

Their reported questions, one at a time, against a stopwatch.

SQL · 1 of 8

Number of rows in inner / left / right / full outer join (join cardinality)

Out loud, in order: clarify, build it, check it, explain it.

Practise SQL

00:00

Your preparation plan

Pick your level and the weeks you have.

Built from what Morgan Stanley asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.

Week 1
SQL
Read: SQL chapters Drill: SQL practice Say it aloud: SQL questions Full plan: Ace the SQL round
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Cloud and tools
Read: Data pipeline chapters Say it aloud: AWS questions
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Data modeling
Read: Data modeling chapters Drill: Modeling practice Say it aloud: Data modeling questions Full plan: Ace the modeling round
Week 2
Rehearse the Morgan Stanley loop
Run the question deckThen repeat your weakest topic once more.

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All interview experiences

Data engineering interview · Finance, Payments & Fintech

Morgan Stanley

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

Stages: 54 with reported questions
Difficulty: Not rated
Cloud: AWS scenarios
Evidence: 2 of 2sources are first-hand

What Morgan Stanley does

And what its data engineers work with.

Business: Investment banking / wealth management
Platform: AWS scenarios; Spark/Hadoop (Sqoop, Hive, HDFS, MapReduce)
Stack named in its interviews: SQL
Python
PySpark
ETL
AWS

The loop

Stages in the order candidates meet them.

01Recruiter
02Technical phone (algo/DS/SQL + testing)
03Final loop (3–5)
04Hiring manager/leader
05Offer

5 stages. 4 rounds have reported questions, described below.

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
SQL
High confidence
- SQL
2 reported questions. See them below
ROUND 02
Python
High confidence
- Python
2 reported questions. See them below
ROUND 03
PySpark / Spark
High confidence
- SQL
- Spark
2 reported questions. See them below
ROUND 04
Big-data ecosystem
Medium confidence
- Spark
- Cloud and tools
2 reported questions. See them below

Reported interview questions

By round. Questions marked Expected are written from the company's standards.

Reported from accounts and guides, by round

SQLHigh confidence

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

PythonHigh confidence

Pseudo-code a dummy ETL in Python with data structures + pandas: extract, cleanse, transform, load to CSVPractise Data Pipeline
Arrays & stacksPractise Python

PySpark / SparkHigh confidence

Spark monitoring & performance managementPractise Apache Spark
Explain and create unit test cases using SQL & PySparkPractise SQL

Big-data ecosystemMedium confidence

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

Expected written from Morgan Stanley's standards

Nobody reported these exact questions at Morgan Stanley. They are the questions a loop for each of its top topics is known to ask, so prepare for them as likely, not as confirmed.

Return the top 3 rows per group, and say what happens on ties.SQL
Find the latest row per key in a table that has duplicates.SQL
What is a shuffle, and how would you make a job shuffle less?Spark
One hot key makes a join slow. What do you do?Spark
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python
When would you choose Airflow, and when would you not?Cloud and tools
How do you load files from object storage into a warehouse?Cloud and tools
Tell me about a pipeline you owned that failed in production.Behavioral
Describe a disagreement about a design, and how it ended.Behavioral

Must know

Ranked by how much of the evidence points at each topic.

SQLReported3 reported questions across 2 rounds
Spark and big dataReported2 reported questions across 2 rounds
Python and codingReported1 reported question across 1 round
Cloud and toolsReported1 reported question across 1 round
Behavioral and communicationIn the prioritiesNamed in the prep priorities
Data modelingIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

SQL (join cardinality, window functions)
Do first (3)
Python pandas ETL
PySpark (monitoring/perf, unit tests); big-data ecosystem (Sqoop/Hive/HDFS/MapReduce)
Data modeling
Then
AWS scenarios
Behavioral

Rejection patterns

Why candidates were turned down, as reported.

Cannot reason about join cardinality
SQL is named in 37 of 50 company accounts.
No testing/CI-CD awareness
Shaky PySpark/ecosystem
Spark is named in 25 of 50 company accounts.
Weak ETL design
Design is named in 27 of 50 company accounts.

Reported experiences

5 reported. Each opens and closes. Composites are labelled.

5 accounts

Data EngineerDate not statedReportedCandidate account
A recruiter call, a technical round (algorithms, data structures, SQL, testing and deployment), then a final loop. Questions included row counts for inner, left, right and full joins, writing a small ETL in pandas to a CSV, Spark monitoring and tuning, unit tests for SQL and PySpark, Hadoop tools, AWS scenarios and arrays and stacks.
What to take from it Know how to test your pipelines, not just write them.
Data EngineerIndia · 2021GeeksforGeeks
Five rounds. The first was an online HackerRank test in four timed parts: SQL coding, multiple choice on Python, SQL and Unix, a data structures problem and database administration scenarios. The author says window functions, joins and database ideas matter most.
What to take from it Prepare for a mixed online test, with a clock.
Data EngineerMumbai, India · Mar 2026Glassdoor
The first round was an online test with two SQL questions and one Python question. The SQL tasks were removing duplicate rows and finding students absent three days in a row. This was in Mumbai in March 2026.
What to take from it Practise duplicates and consecutive-days problems.
Questions named
- Delete duplicate rows
- Students absent for three consecutive days
Data Engineer2022 to 2024Glassdoor
A recruiter call about basic coding, then a spoken coding test in Python, SQL and Scala. Another candidate had a phone call with whiteboard coding and questions on unit and integration tests, deploying to production and finding errors.
What to take from it Be ready to talk about testing and deployment.
Data Engineer2025Glassdoor
A 30-minute interview that ended after 15 minutes. The candidate had a bad experience.
What to take from it Some interviews are short and unclear. Stay calm and ask for feedback.

Sources and how sure we are

Good first-hand signal; testing/CI-CD awareness and join-cardinality reasoning stand out.

GeeksforGeeks — Morgan Stanley Data Engineer 2021
otakutechie — Morgan Stanley Data Engineer 2025 (Round One)

Rehearse the Morgan Stanley loop

Their reported questions, one at a time, against a stopwatch.

SQL · 1 of 8

Number of rows in inner / left / right / full outer join (join cardinality)

Out loud, in order: clarify, build it, check it, explain it.

Practise SQL

00:00

Your preparation plan

Pick your level and the weeks you have.

Built from what Morgan Stanley asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.

Week 1
SQL
Read: SQL chapters Drill: SQL practice Say it aloud: SQL questions Full plan: Ace the SQL round
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Cloud and tools
Read: Data pipeline chapters Say it aloud: AWS questions
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Data modeling
Read: Data modeling chapters Drill: Modeling practice Say it aloud: Data modeling questions Full plan: Ace the modeling round
Week 2
Rehearse the Morgan Stanley loop
Run the question deckThen repeat your weakest topic once more.

What Morgan Stanley does

The loop

What each round expects

SQL

Python

PySpark / Spark

Big-data ecosystem

Reported interview questions

Reported from accounts and guides, by round

Expected written from Morgan Stanley's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Rehearse the Morgan Stanley loop

Your preparation plan

Week 1

Week 2

What Morgan Stanley does

The loop

What each round expects

SQL

Python

PySpark / Spark

Big-data ecosystem

Reported interview questions

Reported from accounts and guides, by round

Expected written from Morgan Stanley's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Rehearse the Morgan Stanley loop

Your preparation plan

Week 1

Week 2