Atlassian's Data Scientist interviews often open with a HackerRank assessment covering SQL, stats, and Python/pandas, followed by SQL-heavy technical rounds, a craft or system-design round, and a behavioral/values conversation, with reported timelines ranging from several weeks to a few months.
$170K
Avg. Base Comp
$270K
Avg. Total Comp
3-5 rounds
Typical Rounds
Not reported
Process Length
Atlassian's data science interview process is notably variable in length and structure, but a few consistent signals show up across candidate reports. Many candidates start with a HackerRank online assessment that mixes SQL, statistics, and Python/pandas questions - one candidate noted the live test expanded well beyond the practice materials Atlassian provided, covering roughly two SQL problems, five stats questions, and around ten pandas data-manipulation tasks in one sitting, so treat the practice portal as a floor rather than the full scope. From there, expect a SQL-heavy technical screen; candidates report questions ranging from straightforward joins and WHERE clauses to business-logic problems like calculating how many users completed a purchase within a set window after viewing content, with interviewers pushing back on join and aggregation choices.
Beyond SQL, interviewers may probe a data science craft or system-design round that goes deeper than syntax - one candidate was asked to implement a statistical analysis algorithm, while another worked through a data-structures problem involving prefix-sum logic and had to defend optimization trade-offs. Case studies also appear: candidates have been asked to reason through data quality issues, metric selection, and business impact (including a web-traffic-decline scenario) rather than just produce correct code.
A behavioral or Atlassian Values round appears in nearly every account, usually separate from technical rounds and focused on collaboration style, handling feedback, and work motivations rather than company trivia. Timelines vary considerably - one candidate described the process stretching across many weeks with long gaps and little recruiter communication, while another described a several-month, pipeline-style process not tied to a specific open role, so build in patience for the wait between rounds.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Atlassian process.
The process at Atlassian felt less like interviewing for a specific role and more like they were evaluating whether you fit their skill set—which actually made it somewhat unpredictable. I went through multiple rounds over several months, starting with a behavioral conversation about what I currently do well, what kind of work energizes me, and what challenges I face. That was followed by a technical round covering SQL and A/B testing concepts, which tested practical experimentation knowledge rather than pure algorithmic depth. There was also a data structures question involving prefix sum logic that required careful explanation of the approach and follow-ups on optimization. One round included a case study—web traffic declining, figure out how you'd approach it—which felt a bit disconnected from my actual domain expertise, but the interviewers seemed to care more about my reasoning process than the specific answer. Then came a principles or values-focused conversation, which felt more like a culture fit assessment. Throughout, feedback was detailed and helped me understand what they were looking for. The overall vibe was that they weren't hiring for a particular open position at the time; they were building a candidate pipeline based on capability. That meant the timeline was long and the outcome uncertain even after all the rounds were done.
Prep tip from this candidate
Prepare for behavioral questions that explore your work style and values, not just accomplishments. Practice SQL and A/B testing scenarios in concrete detail, and be ready to explain algorithmic approaches like prefix sum problems with clear reasoning for trade-offs—Atlassian seems to weight communication and principles as heavily as technical correctness.
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Topics based on recent interview experiences.
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| Question | |
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| Integer to Roman | |
| Dijkstra implementation | |
| Target Indices | |
| Trial User Segmentation | |
| Target Value Search | |
| Concurrent LLM Serving | |
| Decreasing Subsequent Values | |
| Why Do You Want to Work With Us | |
| LRU Cache 1 | |
| Regularization and Validation | |
| Fraudulent Upvotes | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Customer Orders | |
| Comments Histogram | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Subscription Overlap | |
| First to Six | |
| Monthly Customer Report | |
| Upsell Transactions | |
| Experiment Validity | |
| Download Facts | |
| First Touch Attribution | |
| Employee Salaries (ETL Error) | |
| Random SQL Sample | |
| Minimum Change | |
| Compute Deviation |
Synthesized from candidate reports. Individual experiences may vary.
Candidates often start with a HackerRank assessment that can include SQL questions of varying difficulty, several statistics problems, and a Python/pandas data-manipulation section, sometimes alongside a separate questionnaire. One candidate reported the live test covered more ground (including pandas) than the practice materials suggested, and that the BigQuery component took longer than expected, so pacing across sections matters.
After an initial recruiter conversation, candidates report a SQL-focused technical screen built around a realistic business question, such as identifying how many users converted within a specific time window after an event. Interviewers may push back on join logic and aggregation choices with pointed follow-ups, so being able to defend and adjust a query on the spot is more important than getting a single 'correct' answer on the first try.
A deeper technical round may cover data structures and algorithms (one candidate worked through a prefix-sum-style optimization problem), machine learning and statistics theory, or implementing a statistical analysis approach from scratch. Candidates report this round tests reasoning and communication under scrutiny, and in at least one case the interviewer's tone was described as unusually tough, so staying composed while explaining trade-offs is worth practicing.
Several candidates describe a case-study component where the interviewer presents an ambiguous business scenario, such as declining web traffic or a general data problem, and asks the candidate to identify relevant metrics, risks, and next steps. Interviewers appear to care more about the reasoning process, including how candidates discuss data quality and assumptions, than about arriving at one specific answer.
A behavioral round, sometimes explicitly framed around Atlassian's values and sometimes just described as general behavioral questions, typically closes out the process. Candidates report being asked about handling negative feedback, what energizes them at work, and how they collaborate, rather than being quizzed on company-specific trivia. For senior candidates, this may be paired with a separate conversation with a manager who is not necessarily the hiring manager.
