Meta Data Engineer Interview Experience
Meta · Mid level
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.
Interview rounds · 4
- 1
Recruiter screen
BehavioralProject DiscussionI started with a recruiter call that was pretty straightforward, and the recruiter was super helpful about both fit and exactly how to prep for the next rounds.
Q1. Tell me about yourself.
How they answeredI gave a quick overview of my background and recent experience so they could see whether I matched the requirements for a data engineer role with 3+ years of experience.
Q2. Walk me through your experience in your recent company.
How they answeredI walked through the projects I had worked on recently and the kind of data engineering work I had been doing.
Q3. Why Meta?
How they answeredI explained why I wanted Meta specifically, and after that, they felt I was a good fit and moved me forward.
- Q4. How do you handle conflict at work?
- 2
Technical round
SQLCodingTechnicalThe first technical screen was very time-pressured: 30 minutes of SQL and 30 minutes of Python, and I knew I basically needed to get at least 3 out of 5 in each section to move on.
- Q1. Solve an easy SQL question using GROUP BY, HAVING, and basic joins.
- Q2. Solve a medium SQL question using advanced window functions, CTEs, and subqueries.
- Q3. Solve a hard SQL question using subqueries, CTEs, and window functions like LAG and LEAD.
- Q4. Solve Python questions focused on strings, lists, dictionaries, sets, and basic manipulations.
- 3
Other round
BehavioralThe behavioral round was a standard 30-minute screen, and they were checking both motivation and how I handled conflict, impact, and fast learning.
- Q1. Tell me about yourself.
- Q2. Why Meta?
- Q3. Why data engineering?
- Q4. Tell me about a time you had conflict with your team lead or manager and how you resolved it.
- Q5. Tell me about a time you improved a process in the organization that had business impact.
- Q6. Tell me about a time you learned a tool or process very quickly and delivered results.
- 4
Final / onsite round
Product StrategyAnalyticalData ModelingSQLCodingTechnicalThe 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.
Q1. What would you do when there is a customer drop or a specific metric drop in the dashboard for an Instagram-like product?
How they answeredI 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.
Follow-up questions- Do you think this metric should be part of the dashboard while you are doing the root cause analysis?
Q2. Based on your product sense answer, design a data model for that use case.
How they answeredAfter 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.
- Q3. Answer SQL questions based on the case study.
Q4. Answer generic Python questions.
How they answeredThe 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.
Tips from the candidate
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.