Candidates report a timed SQL and Python screen, followed by an onsite with full-stack rounds on data modeling, SQL, Python and product sense, plus an ownership interview. Speed on fundamentals matters.
$170K
Avg. Base Comp
$250K
Avg. Total Comp
5 rounds
Typical Rounds
Not reported
Process Length
Candidates describe the Meta Data Engineer interview as a timed SQL and Python screen followed by a broader onsite. Several report a recruiter call first, then roughly an hour with about five SQL and five Python questions. A few say a minimum number correct in each section was needed to advance, and some who were rejected after the screen said pace, not difficulty, was the problem. SQL topics reported include joins, aggregations, window functions, self-joins and multi-table queries. Python questions lean on dictionaries, lists, sets and basic functions, and one candidate described a book-themed schema.
The onsite is most often described as four interviews: three full-stack rounds and one ownership or behavioral round. Candidates describe the full-stack rounds as mixing product sense, data modeling, SQL and Python, sometimes starting from a product scenario and moving through KPIs to a schema and a query. Others mention ETL design or debugging, and one reported a data-pipeline design round. One candidate expecting an AI-related coding round said the recruiter's materials didn't match the actual round names, so confirm the format with your recruiter.
A few practical takeaways follow. Practice back-to-back SQL and Python under a timer, and narrate your approach and time complexity as you go. Prepare to design a schema from an ambiguous product goal. Have ownership stories ready. Experiences vary by candidate and level, so treat this as a typical pattern, not a fixed script.
Synthesized from 19 candidate reports by our editorial team.
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Real interview reports from people who went through the Meta process.
I came in through a recruiting firm, and my whole process at Meta was one interview with the team lead for the data engineer role. It was non-technical, so there were no coding or SQL problems. The main question I remember was a conceptual one about best practices in data governance. The interview led to an offer, but I was not able to accept it for reasons unrelated to the interview itself. I'd treat my experience as one data point rather than a template. Since my interview skipped the technical side entirely, I can't tell you what a technical round would look like, but I would come ready to explain governance fundamentals clearly and concisely.
Prep tip from this candidate
The only question reported was a conceptual one on data governance best practices, so be ready to explain those fundamentals clearly. This process was non-technical, so don't assume it reflects the technical rounds other candidates may face.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter conversation, sometimes after outreach on LinkedIn. It typically covers background, past projects, comfort with SQL and Python, and scheduling. Some candidates also describe a brief pre-screen with short fundamentals questions, so be ready for quick answers on joins and complexity.
Many candidates describe about five SQL and five Python questions in roughly an hour, often split about evenly. Some say they needed to get at least three right in each section to advance. Reported SQL topics include joins, window functions and aggregations, and Python topics include dictionaries, lists and sets. Pace appears to matter as much as correctness.
Candidates commonly report three full-stack onsite interviews that mix product sense, data modeling, SQL and Python. Some started from a product scenario, defined KPIs, designed a schema and wrote SQL. Dimension choices and ETL trade-offs reportedly drew follow-ups. Question difficulty tends to ramp up within a round.
The onsite typically includes an ownership-focused behavioral conversation. Candidates report questions on past challenges, collaboration, leadership, influencing peers, stakeholder management and why Meta. Prepare specific project stories with clear results. One candidate was also asked what data engineering means to them.
