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Data engineering interview · Big Tech & Enterprise Software

Atlassian

Karat technical screen (SQL + Python), then system design for high-volume event data and a strong business-impact behavioral.

Stages: 44 with reported questions
Difficulty: Not rated
Cloud: AWS
Evidence: 1 of 2sources are first-hand

What Atlassian does

And what its data engineers work with.

Business: SaaS / developer & collaboration tools
Platform: AWS; Spark, dbt-like ELT, Kafka
Stack named in its interviews: SQL
Python
Spark
dbt
Kafka

The loop

Stages in the order candidates meet them.

01Recruiter (30m)
02Karat screen (60m)
03Onsite (SQL, design, values)
04Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Karat technical screen
30–45m coding + 15m review
High confidence
- SQL
- Modeling
- Python
3 reported questions. See them below
ROUND 02
SQL
High confidence
- SQL
2 reported questions. See them below
ROUND 03
System Design
heavily weighted
Medium confidence
- Design
1 reported question. See them below
ROUND 04
Values / Behavioral
Medium confidence
- Behavioral
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

Karat technical screenHigh confidence

SQL: multi-step joins, window functions, event cleaning, aggregationPractise SQL
Snowflake vs star schemaPractise Data Modeling
Python: write a program to find a list of anagramsPractise Python

SQLHigh confidence

Window functions, event cleaning, aggregationPractise SQL
Sessionize product events; top-N; dedupPractise SQL

System DesignMedium confidence

Design reliable, scalable pipelines for high-volume event dataPractise Data Pipeline

Values / BehavioralMedium confidence

Quantifiable business impact of your workPractise Behavioral
Atlassian valuesPractise Behavioral

Expected written from Atlassian's standards

Nobody reported these exact questions at Atlassian. 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
Design a star schema for orders, and state the grain first.Modeling
How do you keep history when a customer's address changes?Modeling
Tell me about a pipeline you owned that failed in production.Behavioral
Describe a disagreement about a design, and how it ended.Behavioral
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
What does a consumer group do, and what happens in a rebalance?Design
Incremental model or full refresh: when does each one fit?Cloud and tools

Must know

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

SQLReported2 reported questions across 2 rounds
Data modelingReported1 reported question across 1 round
Behavioral and communicationReported1 reported question across 1 round
Pipeline and system designReported1 reported question across 1 round
Python and codingReported1 reported question across 1 round
Cloud and toolsIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

Karat-style SQL (window, event cleaning) + Python (anagrams, data manipulation)
Do first (3)
One event-pipeline system design
Business-impact STAR stories
Data modeling (star vs snowflake, partitioning, dbt ELT)
Then
Spark/Kafka

Rejection patterns

Why candidates were turned down, as reported.

Cannot articulate business impact of past work
Weak SQL window functions
SQL is named in 37 of 50 company accounts.
Shallow event-pipeline design
Design is named in 27 of 50 company accounts.

Reported experiences

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

6 accounts

Data EngineerDate not statedReportedCandidate account
A screen run in a Karat style: SQL and Python (window functions, schema questions, anagrams in Python), then rounds on data engineering and architecture. Bring examples of business impact.
What to take from it Say what business result your work had.
Data EngineerSydney, Australia · Apr 2025Glassdoor
In Sydney in April 2025: three SQL questions and one Python question. One SQL topic was a rolling average over the last three days.
What to take from it Practise rolling averages.
Questions named
- Rolling average over the last 3 days
Data EngineerSydney, Australia · May 2025Glassdoor
In Sydney in May 2025: a SQL test on HackerRank, then a technical round on SQL and then Python, which the candidate called pretty easy.
What to take from it Stay calm. The level can be easy.
Senior Data Engineer2024Glassdoor
A senior candidate in 2024 had three programming rounds and two culture rounds. They said no LeetCode questions appeared, and that later rounds needed deep Python and algorithms and, for seniors, system design.
What to take from it Seniors need coding depth and design.
Senior Data Engineer (L5)IndiaExponent
A senior (L5) candidate in India said the interview was about past projects and Spark tuning. They advise making your impact very clear.
What to take from it Prepare numbers that show the impact of your projects.
Data EngineerDate not statedGlassdoor
The values round went through the resume and asked general questions, such as a time you faced a challenge.
What to take from it Have a challenge story ready.
Questions named
- Tell me about a time you faced a challenge

Sources and how sure we are

Good first-hand signal. The Karat screen and the business-impact behavioral are the gates.

Medium — Archana Goyal, "Atlassian Interview Experience — Data Engineering"
InterviewQuery — Atlassian Data Engineer guide (guide, not a first-hand account)

Rehearse the Atlassian loop

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

Karat technical screen · 1 of 8

SQL: multi-step joins, window functions, event cleaning, aggregation

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 Atlassian 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
Data modeling
Read: Data modeling chapters Drill: Modeling practice Say it aloud: Data modeling questions Full plan: Ace the modeling round
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
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
Cloud and tools
Read: Data pipeline chapters Say it aloud: AWS questions
Week 2
Rehearse the Atlassian 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

Atlassian

Karat technical screen (SQL + Python), then system design for high-volume event data and a strong business-impact behavioral.

Stages: 44 with reported questions
Difficulty: Not rated
Cloud: AWS
Evidence: 1 of 2sources are first-hand

What Atlassian does

And what its data engineers work with.

Business: SaaS / developer & collaboration tools
Platform: AWS; Spark, dbt-like ELT, Kafka
Stack named in its interviews: SQL
Python
Spark
dbt
Kafka

The loop

Stages in the order candidates meet them.

01Recruiter (30m)
02Karat screen (60m)
03Onsite (SQL, design, values)
04Offer

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

What each round expects

The topics it covers and the questions candidates reported.

ROUND 01
Karat technical screen
30–45m coding + 15m review
High confidence
- SQL
- Modeling
- Python
3 reported questions. See them below
ROUND 02
SQL
High confidence
- SQL
2 reported questions. See them below
ROUND 03
System Design
heavily weighted
Medium confidence
- Design
1 reported question. See them below
ROUND 04
Values / Behavioral
Medium confidence
- Behavioral
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

Karat technical screenHigh confidence

SQL: multi-step joins, window functions, event cleaning, aggregationPractise SQL
Snowflake vs star schemaPractise Data Modeling
Python: write a program to find a list of anagramsPractise Python

SQLHigh confidence

Window functions, event cleaning, aggregationPractise SQL
Sessionize product events; top-N; dedupPractise SQL

System DesignMedium confidence

Design reliable, scalable pipelines for high-volume event dataPractise Data Pipeline

Values / BehavioralMedium confidence

Quantifiable business impact of your workPractise Behavioral
Atlassian valuesPractise Behavioral

Expected written from Atlassian's standards

Nobody reported these exact questions at Atlassian. 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
Design a star schema for orders, and state the grain first.Modeling
How do you keep history when a customer's address changes?Modeling
Tell me about a pipeline you owned that failed in production.Behavioral
Describe a disagreement about a design, and how it ended.Behavioral
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
What does a consumer group do, and what happens in a rebalance?Design
Incremental model or full refresh: when does each one fit?Cloud and tools

Must know

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

SQLReported2 reported questions across 2 rounds
Data modelingReported1 reported question across 1 round
Behavioral and communicationReported1 reported question across 1 round
Pipeline and system designReported1 reported question across 1 round
Python and codingReported1 reported question across 1 round
Cloud and toolsIn the prioritiesNamed in the prep priorities

What matters most

Do the first group before the second.

Karat-style SQL (window, event cleaning) + Python (anagrams, data manipulation)
Do first (3)
One event-pipeline system design
Business-impact STAR stories
Data modeling (star vs snowflake, partitioning, dbt ELT)
Then
Spark/Kafka

Rejection patterns

Why candidates were turned down, as reported.

Cannot articulate business impact of past work
Weak SQL window functions
SQL is named in 37 of 50 company accounts.
Shallow event-pipeline design
Design is named in 27 of 50 company accounts.

Reported experiences

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

6 accounts

Data EngineerDate not statedReportedCandidate account
A screen run in a Karat style: SQL and Python (window functions, schema questions, anagrams in Python), then rounds on data engineering and architecture. Bring examples of business impact.
What to take from it Say what business result your work had.
Data EngineerSydney, Australia · Apr 2025Glassdoor
In Sydney in April 2025: three SQL questions and one Python question. One SQL topic was a rolling average over the last three days.
What to take from it Practise rolling averages.
Questions named
- Rolling average over the last 3 days
Data EngineerSydney, Australia · May 2025Glassdoor
In Sydney in May 2025: a SQL test on HackerRank, then a technical round on SQL and then Python, which the candidate called pretty easy.
What to take from it Stay calm. The level can be easy.
Senior Data Engineer2024Glassdoor
A senior candidate in 2024 had three programming rounds and two culture rounds. They said no LeetCode questions appeared, and that later rounds needed deep Python and algorithms and, for seniors, system design.
What to take from it Seniors need coding depth and design.
Senior Data Engineer (L5)IndiaExponent
A senior (L5) candidate in India said the interview was about past projects and Spark tuning. They advise making your impact very clear.
What to take from it Prepare numbers that show the impact of your projects.
Data EngineerDate not statedGlassdoor
The values round went through the resume and asked general questions, such as a time you faced a challenge.
What to take from it Have a challenge story ready.
Questions named
- Tell me about a time you faced a challenge

Sources and how sure we are

Good first-hand signal. The Karat screen and the business-impact behavioral are the gates.

Medium — Archana Goyal, "Atlassian Interview Experience — Data Engineering"
InterviewQuery — Atlassian Data Engineer guide (guide, not a first-hand account)

Rehearse the Atlassian loop

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

Karat technical screen · 1 of 8

SQL: multi-step joins, window functions, event cleaning, aggregation

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 Atlassian 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
Data modeling
Read: Data modeling chapters Drill: Modeling practice Say it aloud: Data modeling questions Full plan: Ace the modeling round
Behavioral and communication
Read: Behavioral storytelling Say it aloud: Behavioral questions
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
Cloud and tools
Read: Data pipeline chapters Say it aloud: AWS questions
Week 2
Rehearse the Atlassian loop
Run the question deckThen repeat your weakest topic once more.

What Atlassian does

The loop

What each round expects

Karat technical screen

SQL

System Design

Values / Behavioral

Reported interview questions

Reported from accounts and guides, by round

Expected written from Atlassian's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Rehearse the Atlassian loop

Your preparation plan

Week 1

Week 2

What Atlassian does

The loop

What each round expects

Karat technical screen

SQL

System Design

Values / Behavioral

Reported interview questions

Reported from accounts and guides, by round

Expected written from Atlassian's standards

Must know

What matters most

Rejection patterns

Reported experiences

Questions named

Questions named

Rehearse the Atlassian loop

Your preparation plan

Week 1

Week 2