Updated by Meta candidates
Data Engineer Interview Experience
What stood out to me is that every loop round started with product sense, then I had to turn that answer into a data model, and from that they jumped straight into SQL and Python. One case was even an Instagram metric drop where I had to do root cause analysis live.
Interview process
I got into the process through a referral and started with a recruiter call that was actually very informative. The process was five rounds for me: recruiter, a 60-minute technical screen with 30 minutes SQL and 30 minutes Python, a 30-minute behavioral, and then three loop rounds. The loop was a little different from other data engineer interviews I have done because each round combined product sense, data modeling, SQL, and Python rather than testing them in isolation. The interviewers were super helpful if I spoke out loud and asked clarifying questions, and the recruiters gave me a lot of prep material up front.
- Recruiter screen
- Technical interview
- Other
- Final round
Interview tips
I would say get a strong referral if you can, ask your recruiter every question you have, and be very, very good at SQL because Meta mainly focuses on SQL for data engineering. Practice on a timer because 30 minutes goes really fast, and you need to be able to solve enough questions without getting stuck. For Python, I would focus more on medium-level string, list, dictionary, and set questions, and on syntax, rather than grinding heavy DS&A problems. For data modeling, I used Kimball-style case studies, and for product sense, I watched PM interview videos just to learn how to structure my answer. Most importantly, speak out loud, clarify the question first, and keep behavioral answers precise, avoiding more than five minutes of talk.
Company culture
My read was that Meta is very structured and pretty transparent about the process if you ask. The recruiters gave me a full overview, resources from the company side, and even guidance on what SQL and Python prep should look like. The interviewers felt collaborative rather than adversarial. If I was headed in the right direction, they would add inputs and help me finish the answer. I also felt they want data engineers who can think about the product and business impact, not just write SQL. But SQL is still the main filter, and time management matters a lot.
Questions asked
Overview
The loop was three one-hour rounds, and each one followed the same pattern: about 10 minutes of product sense, then data modeling, then a couple SQL questions and one or two Python questions, with different case studies like an Instagram-like platform, ride sharing, and e-commerce.
Question types asked
Specific questions asked
What would you do when there is a customer drop or a specific metric drop in the dashboard for an Instagram-like product?
Do you think this metric should be part of the dashboard while you are doing the root cause analysis?
I approached it like root cause analysis. I talked through how I would analyze which metric was dropping or rising, what KPIs I would check, and how I would break the problem down before modeling anything. The interviewer was actually super helpful here. If I was going in the right direction, they would suggest an extra metric or hint at something to include, which helped me complete the answer.
Based on your product sense answer, design a data model for that use case.
After the product discussion, I built the data model for that product scenario based on the KPIs and metrics I had identified. That structure was the bridge into the rest of the round.
Answer SQL questions based on the case study.
Answer generic Python questions.
The Python part in these rounds was separate from the actual case study. It was usually one or two questions, and it felt more like a check of core Python comfort than deep algorithmic work.
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