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Data engineering interview · Big Tech & Enterprise Software

Microsoft

Azure-native data engineering: SQL window functions, DS/algo, Spark OOM tuning, and Fabric/ADF/Synapse design.

Stages: 64 with reported questions
Difficulty: Not rated
Cloud: Azure-native
Evidence: 2 of 3sources are first-hand

What Microsoft does

And what its data engineers work with.

Business: Cloud / enterprise software
Platform: Azure-native: Data Factory, Synapse, Microsoft Fabric, ADLS, Event Hubs, Cosmos DB
Stack named in its interviews: SQL
Python
Spark
Azure (ADF/Synapse/Fabric)
Data modeling

The loop

Stages in the order candidates meet them.

01Recruiter
02Technical phone
03Onsite loop
04System design (senior)
05Behavioral
06Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Round 1 (typical)
Medium confidence
- SQL
3 reported questions. See them below
ROUND 02
Python / DSA
Medium confidence
- Python
3 reported questions. See them below
ROUND 03
Spark / Big Data
High confidence
- Spark
1 reported question. See them below
ROUND 04
Azure
High confidence
- SQL
- Cloud and tools
3 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

Round 1 (typical)Medium confidence

Data structures (easy–medium)
SQL (window functions)Practise SQL
Project discussion

Python / DSAMedium confidence

Arrays, hashmaps, strings, linked listsPractise Python
Sliding window / two-pointerPractise Python
Parse logs, transform JSON, clean messy dataPractise Python

Spark / Big DataHigh confidence

Optimize a Spark job processing 1TB that runs out of memoryPractise Apache Spark

AzureHigh confidence

How familiar are you with Microsoft Fabric and how it changes DE?
Cosmos DB vs SQL Azure vs ADLSPractise SQL
ADF orchestration; batch vs streaming (Event Hubs); ETL vs ELTPractise Airflow

Expected written from Microsoft's standards

Nobody reported these exact questions at Microsoft. 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
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
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
Design a daily pipeline from an app database to a warehouse that is safe to rerun.Design
How would you handle late and duplicate events?Design
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python

Must know

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

SQLReported2 reported questions across 2 rounds
Cloud and toolsReported1 reported question across 1 round
Spark and big dataReported1 reported question across 1 round
Pipeline and system designIn the prioritiesNamed in the prep priorities
Python and codingIn the prioritiesNamed in the prep priorities
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

SQL window functions; DS/algo (arrays/hashmaps/sliding window)
Do first (3)
Spark OOM optimization
Azure-native design (ADF, Synapse, Fabric; ETL vs ELT; Cosmos vs SQL Azure vs ADLS)
Data modeling
Then
Python data manipulation
Behavioral (growth mindset)

Rejection patterns

Why candidates were turned down, as reported.

Weak SQL window functions
SQL is named in 37 of 50 company accounts.
Cannot optimize a Spark OOM
Spark is named in 25 of 50 company accounts.
Thin Azure-native knowledge (Fabric/ADF/Synapse)
Cloud and tools is named in 4 of 50 company accounts.

Reported experiences

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

6 accounts

Data EngineerDate not statedReportedCandidate post
The early rounds were heavy on SQL. Later rounds were about system design with Azure tools such as Data Factory, Synapse and Fabric.
What to take from it Learn what a pipeline looks like on Azure.
Software Engineer 2 (Data)Date not statedR1 & R2Candidate account
A software engineer 2 (data) candidate got data structures, SQL with window functions, a talk about projects and coding. So even the data track has a coding bar.
What to take from it Do not skip data structures practice.
Data EngineerDate not statedInterview Query (candidate-reported items)
Tasks reported: change a SQL query into PySpark, drop rows with nulls, then find the most active users each day.
What to take from it Practise writing the same logic in SQL and in PySpark.
Questions named
- Convert a SQL query to PySpark
- Top users by daily activity
Data EngineerDate not statedInterview Query (candidate-reported items)
Questions asked about normalised against flat tables, Z-ordering, and incremental loads in Azure Data Factory.
What to take from it Know what an incremental load is and how to set one up.
Questions named
- Normalisation vs denormalisation
- Z-ordering
- Incremental loads in Azure Data Factory
Data EngineerDate not statedInterview Query (candidate-reported items)
The rounds covered data modeling, system design, coding and practical problem solving. One question asked when to use a hash map, a queue or a stack.
What to take from it Expect basic data structure choices as well as data topics.
Data EngineerDate not statedInterview Query (candidate-reported items)
Behavioral questions covered working across teams and learning from feedback.
What to take from it Have one story about feedback you used.

Sources and how sure we are

Good first-hand signal. Azure-native depth (Fabric/ADF/Synapse) plus SQL and Spark OOM tuning are the differentiators.

Medium — "My Microsoft Data Engineering Interview"
Medium — Kaizen, "Microsoft SE2 (Data) Interview (R1&R2)"
InterviewQuery — Microsoft Data Engineer guide (guide, not a first-hand account)

Rehearse the Microsoft loop

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

Round 1 (typical) · 1 of 10

Data structures (easy–medium)

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

00:00

Your preparation plan

Pick your level and the weeks you have.

Built from what Microsoft 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
Cloud and tools
Read: Data pipeline chapters Say it aloud: Azure questions
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the Microsoft loop
Run the question deckThen repeat your weakest topic once more.

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All interview experiences

Data engineering interview · Big Tech & Enterprise Software

Microsoft

Azure-native data engineering: SQL window functions, DS/algo, Spark OOM tuning, and Fabric/ADF/Synapse design.

Stages: 64 with reported questions
Difficulty: Not rated
Cloud: Azure-native
Evidence: 2 of 3sources are first-hand

What Microsoft does

And what its data engineers work with.

Business: Cloud / enterprise software
Platform: Azure-native: Data Factory, Synapse, Microsoft Fabric, ADLS, Event Hubs, Cosmos DB
Stack named in its interviews: SQL
Python
Spark
Azure (ADF/Synapse/Fabric)
Data modeling

The loop

Stages in the order candidates meet them.

01Recruiter
02Technical phone
03Onsite loop
04System design (senior)
05Behavioral
06Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Round 1 (typical)
Medium confidence
- SQL
3 reported questions. See them below
ROUND 02
Python / DSA
Medium confidence
- Python
3 reported questions. See them below
ROUND 03
Spark / Big Data
High confidence
- Spark
1 reported question. See them below
ROUND 04
Azure
High confidence
- SQL
- Cloud and tools
3 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

Round 1 (typical)Medium confidence

Data structures (easy–medium)
SQL (window functions)Practise SQL
Project discussion

Python / DSAMedium confidence

Arrays, hashmaps, strings, linked listsPractise Python
Sliding window / two-pointerPractise Python
Parse logs, transform JSON, clean messy dataPractise Python

Spark / Big DataHigh confidence

Optimize a Spark job processing 1TB that runs out of memoryPractise Apache Spark

AzureHigh confidence

How familiar are you with Microsoft Fabric and how it changes DE?
Cosmos DB vs SQL Azure vs ADLSPractise SQL
ADF orchestration; batch vs streaming (Event Hubs); ETL vs ELTPractise Airflow

Expected written from Microsoft's standards

Nobody reported these exact questions at Microsoft. 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
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
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
Design a daily pipeline from an app database to a warehouse that is safe to rerun.Design
How would you handle late and duplicate events?Design
Group events into sessions with a 30-minute gap.Python
Count by key in a file bigger than memory.Python

Must know

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

SQLReported2 reported questions across 2 rounds
Cloud and toolsReported1 reported question across 1 round
Spark and big dataReported1 reported question across 1 round
Pipeline and system designIn the prioritiesNamed in the prep priorities
Python and codingIn the prioritiesNamed in the prep priorities
Behavioral and communicationIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

SQL window functions; DS/algo (arrays/hashmaps/sliding window)
Do first (3)
Spark OOM optimization
Azure-native design (ADF, Synapse, Fabric; ETL vs ELT; Cosmos vs SQL Azure vs ADLS)
Data modeling
Then
Python data manipulation
Behavioral (growth mindset)

Rejection patterns

Why candidates were turned down, as reported.

Weak SQL window functions
SQL is named in 37 of 50 company accounts.
Cannot optimize a Spark OOM
Spark is named in 25 of 50 company accounts.
Thin Azure-native knowledge (Fabric/ADF/Synapse)
Cloud and tools is named in 4 of 50 company accounts.

Reported experiences

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

6 accounts

Data EngineerDate not statedReportedCandidate post
The early rounds were heavy on SQL. Later rounds were about system design with Azure tools such as Data Factory, Synapse and Fabric.
What to take from it Learn what a pipeline looks like on Azure.
Software Engineer 2 (Data)Date not statedR1 & R2Candidate account
A software engineer 2 (data) candidate got data structures, SQL with window functions, a talk about projects and coding. So even the data track has a coding bar.
What to take from it Do not skip data structures practice.
Data EngineerDate not statedInterview Query (candidate-reported items)
Tasks reported: change a SQL query into PySpark, drop rows with nulls, then find the most active users each day.
What to take from it Practise writing the same logic in SQL and in PySpark.
Questions named
- Convert a SQL query to PySpark
- Top users by daily activity
Data EngineerDate not statedInterview Query (candidate-reported items)
Questions asked about normalised against flat tables, Z-ordering, and incremental loads in Azure Data Factory.
What to take from it Know what an incremental load is and how to set one up.
Questions named
- Normalisation vs denormalisation
- Z-ordering
- Incremental loads in Azure Data Factory
Data EngineerDate not statedInterview Query (candidate-reported items)
The rounds covered data modeling, system design, coding and practical problem solving. One question asked when to use a hash map, a queue or a stack.
What to take from it Expect basic data structure choices as well as data topics.
Data EngineerDate not statedInterview Query (candidate-reported items)
Behavioral questions covered working across teams and learning from feedback.
What to take from it Have one story about feedback you used.

Sources and how sure we are

Good first-hand signal. Azure-native depth (Fabric/ADF/Synapse) plus SQL and Spark OOM tuning are the differentiators.

Medium — "My Microsoft Data Engineering Interview"
Medium — Kaizen, "Microsoft SE2 (Data) Interview (R1&R2)"
InterviewQuery — Microsoft Data Engineer guide (guide, not a first-hand account)

Rehearse the Microsoft loop

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

Round 1 (typical) · 1 of 10

Data structures (easy–medium)

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

00:00

Your preparation plan

Pick your level and the weeks you have.

Built from what Microsoft 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
Cloud and tools
Read: Data pipeline chapters Say it aloud: Azure questions
Spark and big data
Read: Spark chapters Drill: Spark playground Say it aloud: Spark questions Full plan: Ace the Spark round
Pipeline and system design
Read: Data pipeline chapters Drill: Pipeline practice Say it aloud: Data pipeline questions Full plan: Ace the pipeline round
Python and coding
Read: Python chapters Drill: Python practice Say it aloud: Python questions Full plan: Ace the Python round
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
Week 2
Rehearse the Microsoft loop
Run the question deckThen repeat your weakest topic once more.

What Microsoft does

The loop

What each round expects

Round 1 (typical)

Python / DSA

Spark / Big Data

Azure

Reported interview questions

Reported from accounts and guides, by round

Expected written from Microsoft's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Rehearse the Microsoft loop

Your preparation plan

Week 1

Week 2

What Microsoft does

The loop

What each round expects

Round 1 (typical)

Python / DSA

Spark / Big Data

Azure

Reported interview questions

Reported from accounts and guides, by round

Expected written from Microsoft's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Rehearse the Microsoft loop

Your preparation plan

Week 1

Week 2