Meta Data Engineer interview typically runs 4 rounds: recruiter screen, SQL/Python screen, technical round, onsite. It usually takes a few weeks and is known for a high bar on fundamentals and speed.
$160K
Avg. Base Comp
$227K
Avg. Total Comp
4-5
Typical Rounds
3-5 weeks
Process Length
We’ve seen Meta screen Data Engineer candidates for fast, correct fundamentals more than for polished storytelling. Multiple candidates reported SQL and Python questions that were short, direct, and time-sensitive, with a clear expectation that you can move quickly through joins, filtering, dictionaries, lists, and basic complexity concepts without getting lost in theory. Even when the questions were described as practical rather than tricky, the bar was unforgiving: one candidate noted they needed to hit a minimum number correct in each section, and another said the Python portion exposed gaps in everyday implementation work, not just algorithm knowledge.
A recurring theme is that Meta wants people who can explain applied decisions in context. Candidates repeatedly mentioned being pressed on real projects, large datasets, and how they turned analysis into business insight, not just whether they knew the right syntax. That matters because the company seems to use the interview to separate people who can write queries from people who can reason about data systems and communicate tradeoffs crisply. We also noticed that even candidates with prior Meta experience still had to prove themselves from scratch, which suggests the process is less about tenure or familiarity and more about demonstrating current, hands-on capability.
The non-obvious make-or-break factor here is consistency across fundamentals. Our candidates who felt strongest on SQL still struggled when Python questions shifted into writing functions from scratch or building small data structures like graphs and in-degree tracking. Meta’s pattern is clear: they reward candidates who are fluent in the basics and can apply them cleanly under pressure, especially when the prompt looks simple but requires precise execution.
Synthetized from 3 candidates reports by our editorial team.
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Real interview reports from people who went through the Meta process.
The first screen was more intense than I expected for a Data Engineer role. It was a phone interview split between SQL and Python, with 5 SQL questions and 5 Python questions, and I was told I needed to get at least 3 correct in each section to move forward. The SQL was pretty practical, including an employee database-style question and a join-tables problem where I had to retrieve the right results under time pressure. The Python side leaned more toward LeetCode-style basics, especially dictionaries and lists, so it felt less like coding something huge and more like showing that I could reason quickly about common data structures.
Before that, there was also a short HR/pre-screen style round that was very basic and fast, around 15 minutes. That one was mostly one-word or short-answer questions on SQL and Python fundamentals, like time complexity, joins, space complexity, boolean values, and which clause is used to filter or select unique rows. I also got a standard behavioral question about why I wanted to work at Meta, and another round that was more conversational with tell me about yourself and why should we hire you. The vibe was generally friendly and smooth, but the bar was clearly set around speed and accuracy on fundamentals. If you pass the screen, the onsite is virtual and has 4 rounds: 3 full-stack rounds and 1 ownership round. I ended up not getting an offer, and the main takeaway for me was that Meta seemed to care a lot about crisp SQL answers and being able to respond quickly on Python basics without overexplaining.
Prep tip from this candidate
Drill fast SQL fundamentals like joins, filtering, and selecting distinct rows, and practice answering Python data-structure questions in one- or two-word form, especially time complexity and dictionary/list basics. Be ready for a timed screen where you need to hit a minimum number correct rather than just talk through your thinking.
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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.
A short HR or recruiter pre-screen to confirm background, motivation, and basic fit for the Data Engineer role. Candidates reported very basic questions on SQL and Python fundamentals, plus standard behavioral prompts like why they wanted to work at Meta and tell me about yourself.
A phone interview focused heavily on SQL and Python. Candidates described practical SQL problems such as joins and employee-database style queries, along with Python questions that tested dictionaries, lists, time complexity, and basic implementation skills under time pressure.
A more conversational round where interviewers dug into how candidates had used SQL and Python in real projects. This stage emphasized explaining your thinking clearly, discussing large datasets, the tools used, challenges encountered, and how you worked with cross-functional teams to turn analysis into business insights.
Candidates who passed the screen reported a virtual onsite with four rounds: three full-stack technical rounds and one ownership round. The loop was described as broad and rigorous, with additional emphasis on SQL, Python, data modeling, and practical problem solving.