Real, anonymous reports from people who interviewed for Data Engineer at Atlassian, newest first and distilled into what to expect across the loop.
After a recruiter call, I was slotted into an online interview. The process felt pretty focused on core coding skills: I ended up doing a technical round that mixed SQL and a DSA-style problem. My SQL portion was a single SQL question, and the algorithm question ended up being a LeetCode-easy level.
My interview started with a HackerRank-style assessment made entirely of SQL. After that, I went into a technical section that was again centered on SQL followed by Python, and it honestly felt pretty manageable.
Candidates can expect multiple technical rounds focused on SQL and Python, with questions ranging from basic syntax to more complex problem-solving tasks. Familiarity with SQL patterns and Python programming is essential for success in these rounds.
The interview process includes a behavioral round that emphasizes cultural fit and personal work style, often involving discussions about past experiences rather than technical challenges. Candidates should prepare to articulate their values and work philosophy.
Atlassian employs a panel review process where multiple interviewers assess candidates, which can create a more formal evaluation atmosphere. Candidates may find this approach offers a clearer rationale for decisions made.
Candidates generally receive timely feedback after interviews, although experiences with recruiter communication can vary significantly. Some candidates report a lack of updates, which can lead to uncertainty about their standing in the process.
The overall difficulty of the interview process is perceived as average, with some candidates feeling that the technical challenges were manageable. However, experiences can vary, with some candidates finding certain rounds unexpectedly intense or poorly structured.
For more senior roles, candidates may encounter system design questions and conceptual discussions related to data modeling, such as differences between snowflake and star schemas. Preparation for these topics is recommended for those aiming for higher-level positions.