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Big Tech & Enterprise Software

Atlassian

SaaS / developer & collaboration tools · Data Engineering interview

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

Cloud / platform

AWS; Spark, dbt-like ELT, Kafka

Reported difficulty

Not reported

SQLPythonSparkdbtKafka

Hiring process

Recruiter (30m)

Karat screen (60m)

Onsite (SQL, design, values)

Offer

Round-by-round breakdown

Karat technical screen

· 30–45m coding + 15m reviewHigh confidence

SQL: multi-step joins, window functions, event cleaning, aggregation
Snowflake vs star schema
Python: write a program to find a list of anagrams

SQL

High confidence

Window functions, event cleaning, aggregation
Sessionize product events; top-N; dedup

System Design

· heavily weightedMedium confidence

Design reliable, scalable pipelines for high-volume event data

Values / Behavioral

Medium confidence

Quantifiable business impact of your work
Atlassian values

System & pipeline design prompts

Scalable high-volume event-data pipeline (late/duplicate data, dbt ELT, cost)

Prep — high priority

Karat-style SQL (window, event cleaning) + Python (anagrams, data manipulation)
One event-pipeline system design
Business-impact STAR stories

Prep — medium priority

Data modeling (star vs snowflake, partitioning, dbt ELT)
Spark/Kafka

Rejection patterns

Cannot articulate business impact of past work
Weak SQL window functions
Shallow event-pipeline design

Reported experiences

Data Engineer

ReportedFirst-hand

Karat-style SQL+Python screen (window functions, schema questions, anagrams Python), then DE/architecture-focused rounds; bring business impact.

Rehearse for Atlassian

Answered questions on what Atlassian 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