Atlassian interview notes, page 7 of 11

Large Enterprises

Most of these Atlassian write-ups come from Software Engineers (60), then Machine Learning Engineers (28) and Data Scientists (12). The most reported rounds are the technical phone screen (45), the system design round (38), the behavioral / leadership round (10) and the onsite coding round (3).

104 interview notes · updated Aug 2026

Atlassian·Data Scientist·Technical Phone Screen

May 2026

SQL technical screen for a Data Scientist role at Atlassian. One question, medium difficulty, e-commerce data with a couple of tables. Pretty standard stuff but the date filtering piece is where people slip up.

Given a users table and an orders table, write a SQL query that returns the top 3 users by total spend in the last 30 days. Output should include user_id, user name, and total amount spent.

“I got the join and the aggregation fine but almost forgot the date filter entirely.”

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Atlassian·Software Engineer·Technical Phone Screen

Jun 2026

Atlassian coding screen that looked straightforward on paper but kept expanding. The base problem was simple enough, but the follow-up directions made it clear they were stress-testing how you think about evolving an API, not just whether you can write a loop.

Write a function that takes a list of URLs, sends an HTTP GET request to each one sequentially, and returns the results. Each result should include the URL, the status code, and the response body. Then discuss how you'd handle timeouts, propagate errors, and how the function signature would need to change as requirements evolve (deduplication, parallelization).

“Started fine.”

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Atlassian·Software Engineer·Technical Phone Screen

Jun 2026

Atlassian coding round focused entirely on a rate limiter problem. Started broad and kept getting pushed to simplify and justify design decisions, which honestly felt more like a design interview than a coding one.

Implement a rate limiter with a shouldPass(int timeBucket) method. Start with a general solution that tracks requests across a sliding window of x buckets and rejects if count exceeds threshold y.
Simplify the rate limiter to handle a single time bucket instead of a rolling window. How does the implementation change?
Should requests that get rejected still count toward the rate limit window for future requests? Justify your decision.
What are the memory implications of using a list versus a deque versus a rolling array for tracking request timestamps in a rate limiter?

“I jumped straight to the sliding window with a deque before they even finished the prompt, which felt good in the moment but I skipped explaining my reasoning.”

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Atlassian·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Atlassian focused entirely on diagnosing and fixing a slow recipe lookup service. The question had a lot of surface area and I felt like I was sprinting through topics the whole time.

You have a recipe lookup and serving system that's too slow. Walk through how you'd reduce end-to-end latency, covering caching at multiple layers, database read scaling, query and index tuning, payload shaping, and async vs sync paths. Also discuss where you'd instrument and measure, and how you'd prioritize given an SLO target.

“Big question.”

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Atlassian·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026

ML system design round at Atlassian for an MLE role, roughly 30 minutes, focused entirely on designing a recommendation system end to end. Breadth over depth was clearly the expectation, which took me a minute to calibrate to.

Design a recommendation system end to end, covering retrieval, ranking, features, training, and serving.
What metrics would you use to evaluate the recommendation system offline and online?
How would you handle feature engineering and the feature store for this system?
Walk me through how you'd set up the online serving infrastructure for low-latency recommendations.

“30 minutes is not a lot of time for this scope.”

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Atlassian·Data Scientist·Technical Phone Screen

Jun 2026

Atlassian data science interview with a classic metric drop scenario. One meaty case question that basically asked you to think out loud about a checkout rate problem under time pressure. Felt more like a business consulting exercise than a pure stats interview.

Checkout completion rate dropped 7% yesterday while overall traffic held steady. Walk through how you'd diagnose the root cause, what analyses you'd run, and how you'd prioritize fixes.

“I started with segmentation which felt right, but I jumped to A/B flag changes too fast and the interviewer had to nudge me back to check the upstream funnel first.”

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Atlassian·Data Scientist·Technical Phone Screen

Jun 2026

SQL heavy technical screen for a Data Scientist role at Atlassian. One question, but it had a lot of layers: bidirectional game data, window functions, deterministic tie-breaking. The kind of problem where you think you have it and then realize you forgot to union the opponent's perspective.

Given a games table where each row represents one team's perspective in a matchup, write a SQL query that treats every game from both teams' viewpoints, then returns each team's top 3 highest-scoring games for the 2024 season. Include team name, opponent name, team score, and a rank column. Break ties by score descending, then date descending, then game_id ascending.

“The union step is where I almost tripped up.”

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Atlassian·Software Engineer·Technical Phone Screen

Jun 2026

Atlassian technical phone screen with a follow-up twist on an async HTTP fetching problem. The deduplication angle plus the testing question made it feel less like a coding exercise and more like a design conversation.

You have a list of URLs to fetch sequentially. Deduplicate the list first so each unique URL is only requested once, preserving the order of first occurrence. How would you implement this, and how would you test it?

“The dedup part was fine, set plus insertion-order tracking, nothing crazy.”

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Atlassian·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Atlassian system design round, rapid-fire format where they throw a scenario at you and expect you to just start talking through it methodically. Not a lot of back-and-forth, more like they're watching how your brain works under pressure.

A system starts slowing down noticeably after you scale it up by adding more nodes, replicas, or load. How do you troubleshoot it?

“This one is deceptively wide.”

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Atlassian·Software Engineer·Technical Phone Screen

Jun 2026

Atlassian SWE interview with a classic interval merging problem. The follow-up variants were the real test and I wasn't fully prepared for them.

Given an array of intervals, merge all overlapping ones and return the resulting non-overlapping intervals.
How would you handle a streaming version of this problem, where intervals arrive one at a time and you need to support queries on the current merged set?
If you already have a fully merged list of intervals, how do you efficiently insert a new interval into it?

“Sort by start time then sweep through, merging as you go.”

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