Real, anonymous reports from people who interviewed for Data Engineer at Meta, newest first and distilled into what to expect across the loop.
After a recruiter outreach, I worked through a pretty structured sequence of rounds that mostly centered on speed and clear problem-solving. The recruiter call was mainly role-fit and process details, and then I moved into a technical screening where I had to complete multiple SQL and Python tasks under tight time limits. Across the screening, I was expected to keep going until each task was fully resolved with the expected results, and the interviewers kept pulling me back to my thought process, including time complexity when relevant. The pattern felt consistent: SQL and Python were both tested with a mix of practical concepts like joins, aggregations, window functions, and data-structure style coding, and the hard part was pacing—there wasn’t really room to debug slowly.
After a recruiter outreach, I got an initial technical screen that was basically a live coding session on a platform with my screen shared to the interviewer. It ran about 45 minutes, and it was split evenly between Python and SQL. The interviewer stayed engaged and was genuinely helpful when I got stuck, which made the pace feel less punishing than I expected.
The interview process typically begins with a recruiter call focused on assessing fit for the Data Engineer role, including a few light technical questions to gauge foundational knowledge. This step is brief but crucial for determining whether to proceed to technical evaluations.
Candidates face a technical screening that is time-boxed, usually split between SQL and Python tasks, requiring quick problem-solving under pressure. The emphasis is on practical SQL concepts like joins and aggregations, alongside Python algorithmic tasks, with a focus on speed and correctness.
Following the technical screen, candidates participate in behavioral interviews that assess communication skills and decision-making under pressure, often framed around real-world product scenarios and data modeling. These rounds evaluate how candidates translate product goals into technical solutions.
In later rounds, candidates may encounter system design discussions and broader technical evaluations that go beyond coding to assess data pipeline thinking and collaboration skills. This stage emphasizes the ability to reason about data in a real-world context.
Throughout the interview process, candidates are expected to manage their time effectively, as the pace is often tight and the number of questions is high. Success is heavily influenced by the ability to maintain momentum and produce results quickly, with limited opportunities to debug or iterate.
Candidates report a demanding but structured experience, where feedback often centers on communication and problem-solving speed. While the process can feel intense, many appreciate the clarity and organization of the interview stages, even if they do not receive an offer.