Meta Data Engineer candidates commonly report a recruiter conversation, a timed SQL-and-Python screen, then an onsite that combines full-stack data cases or technical rounds with ownership and behavioral discussion.
$178K
Avg. Base Comp
$244K
Avg. Total Comp
4 rounds
Typical Rounds
2 months
Process Length
Meta Data Engineer interviews reported here place unusual emphasis on working quickly across SQL, Python, data modeling, and product thinking. Several candidates describe an initial recruiter conversation followed by a roughly one-hour technical screen. Reported formats vary: some faced five SQL and five Python questions, while others describe shorter blocks for each language. SQL topics include joins, aggregations, subqueries, NULL handling, window functions, and multi-table queries; Python work centers on practical data structures, functions, list manipulation, and dictionaries.
Candidates who reached the onsite often report four back-to-back interviews, though the exact composition differs by report. Some describe three full-stack sessions plus an ownership round; others describe distinct SQL/ETL, data-modeling, product-oriented, and behavioral conversations. Product cases can begin with a feature or marketplace scenario and ask the candidate to define KPIs, model the data, and write SQL. Modeling follow-ups may probe schema and dimension choices, while ETL discussions can cover raw-data transformations, pipeline design, or query optimization.
Prepare timed mixed SQL/Python practice rather than treating the screen as a single deep coding problem. For onsite preparation, rehearse explaining how a vague product goal becomes a measurable metric, a defensible relational or dimensional model, and a correct query. Have concise ownership examples ready for collaboration, stakeholder management, influence, and past challenges. Reported formats are not fully uniform, so confirm the scheduled rounds with your recruiter.
Synthesized from 15 candidate reports by our editorial team.
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Real interview reports from people who went through the Meta process.
I was preparing for a virtual onsite for a Meta Data Engineer role, and the most confusing part was that my recruiter did not schedule a prep call and the interview document in the career portal did not seem to line up with the round names. From the guidance I received, I expected the onsite to include an AI debugging round, a full-stack round, and a behavioral round. The AI-focused portion sounded like the most unusual piece, so I focused my preparation on Meta-tagged AI coding problems from DataDriven rather than assuming this would be a standard data engineering interview. I would make sure to clarify the exact round names and expectations with the recruiter ahead of time, since the portal materials were not especially clear.
Prep tip from this candidate
Confirm the actual onsite round names and expectations with the recruiter, since the portal materials did not match this candidate's schedule.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an introductory recruiter conversation covering background, projects, role expectations, availability, and sometimes current SQL/Python experience. One report notes that a recruiter may also clarify the upcoming process, but candidates should confirm their own schedule.
Multiple candidates report a mixed SQL/Python screen completed under significant time pressure. Reported SQL includes joins, aggregations, subqueries, NULL handling, window functions, and multi-table queries; Python includes dictionaries, lists, functions, and other core data structures.
Candidates who advanced describe full-stack or separate technical conversations that may move from a product scenario to KPI definition, data modeling, and SQL. Reports also mention ETL-pipeline design, raw-data transformations, query optimization, and explaining schema trade-offs.
Several onsite reports include a dedicated ownership or behavioral conversation. Candidates report questions about past challenges, leadership style, collaboration, stakeholder management, initiative, and influence; some full-stack sessions also include behavioral discussion.
