Meta Data Engineer Interview Guide
Prepfully’s Meta DE experts who are Staff and Senior DEs helped build this guide with their experience conducting mocks on our platform and interviewing candidates for Meta. This guide covers the full loop and lists recently reported questions for each of them.
The rounds for Meta for the Data Engineering interview loop are:
- Recruiter screen
- Technical screen
- Onsite:
3 x Full Stack interviews
Ownership interview - Hiring Committee and Team Matching
The round names we're using are official and verbatim from the material Meta uses for this interview (which we have thanks to our Meta DE coaches). Much of this guide treats that PDF as gospel and features targeted tips from our coaches so you can deliver on what Meta internally expects of you.
Recruiter screen
This is a 30-minute screen where you'll talk to your recruiter about your
- background
- experience
- long-term goals,
- why Meta
and other relevant details so they can lock in a level that sets the bar for how you’ll be evaluated for the rest of the loop.
You should be prepared for a couple of entry level SQL and Python questions and a discussion of the stack that you've worked with.
There are 2 things to do on this call:
- Settle your coding language. Our coaches say that if you choose Scala, the coding challenge will have nothing to do with Scala as a Spark construct and you’ll need to be fluent in it as a pure functional language. We have to put that down in writing because people have found out the hard way.
- Establish a good rapport with your recruiter by being upfront about your background and what you want out of your career. They’re known to advocate for a candidate later in the process so they're good people to have on your side.
Technical screen
This is a 60 minute screen with a Meta DE and the timeframe approximately breaks down into:
- 5 min: introduction
- 25 min: SQL
- 25 min: coding
- 5 min: your questions
The SQL runs against a PostgreSQL database, though Meta states that standard ANSI SQL is enough to pass. You’ll work in CoderPad in a plain editor with no autocomplete or IDE.
The SQL scope has been officially defined as: joins, subqueries, aggregations, filters, case statements, etc. You’ll be asked to write SQL to answer various business questions. Additionally, they’ll also test how you handle errors and NULL values. Refer to our Amazon Data Engineer guide for in-depth SQL tips.
The reported SQL screen questions are:
- What is the difference between UNION and UNION ALL? Which is faster?
- Given an order table, write the queries to generate the required output.
- Write a query to sort a particular column in a given table.
- Find the top 5 sales products from the order table.
- For a given Sales table, compare the percentage of total sales on the first and last working day of the month.
For Python, the basics of loops, lists, strings, dictionaries, etc are non-negotiable. The screen will assess how you approach the problem and how you use the information you’re given to come up with a solution. The interviewers will also make a note of your speed and efficiency while solving.
Recently reported questions for coding:
- Given a list, replace the NULL values with the previous value in the list.
- Write a code to display the occurrence of a particular character in a given string.
- Given a list of integers, work out a solution to find whether the list is monotonic (increasing or decreasing) or not.
Collecting tips across mock interviews for DEs, we have:
- Talk through the algorithm before you write it down, because if you change your mind halfway, that's where you end up losing a lot of time.
- You’re fully allowed to ask the interviewer for any syntax you might’ve forgotten.
- Listen carefully for any hint-shaped questions that the interviewer might throw at you, like, “Are you sure you want to use a loop there?”
Meta Data Engineering Manager Onsite Interviews
If you clear both the screens, you’ll sit through:
- 3 x Full Stack interviews, 60 minutes each
- Ownership interview, 30 minutes
The video covers every round of the Meta Data Engineer interview loop with specific tips to do your best in each of them
1. Full Stack Interview
What we have to point out here is that the 3 technical hours are not 3 different interviews. Each one is the same interview run against a different product scenario, cut into 4 graded segments:
- 10 min of product sense
- 15 min of data modeling,
- 15 min SQL and
- 15 min of Python.
All 3 technical interviews are case studies of typical product challenges that Meta solves with data. What the interviewer will focus on is to see if you can think critically about what the product needs and turn it into a robust technical solution.
Product sense opens with open-ended questions about a product which can sometimes NOT belong to Meta. It’s possible you get a prompt about a rideshare algorithm or Netflix even. When it does belong to Meta, Facebook Marketplace and Instagram Reels are the ones candidates report the most.
To prepare, we suggest that you read a large company's financial statements and figure out the metrics that are their lifeblood. With that context, devise a strategy for how you'd calculate each one and what you’d do if they started moving unexpectedly. For Meta specifically you can find their quarterly results here.
Our Meta PM guide discusses Product sense in detail. While such depth is not expected of you, you’ll get a sense of the prompts and how to approach them.
The data modeling section is where you’ll be tasked with brainstorming the data needs of a user product. You'll then design a data model to support analytics use cases and write select SQL statements to produce specific results. Essentially, you'll have to answer to the metrics from the previous segment and defend those choices. This is the part where you should expect a lot of follow-ups, especially about your choice of dimension types.
Our coaches suggest that you go through the major products you use on the daily and ask yourself how you would model each function that you use of theirs. Go into the details of creating logging designs for how data should be captured, and then design data models to support analytical queries and reporting needs for them.
Meta says that for SQL they'll ask you to focus on basic SQL constructs and the scope matches with the technical screen’s. There have been some disagreeing reports from candidates about whose schema the SQL segment runs against. Some report queries written against the model they built earlier, and the others report predefined tables that the interviewer gave to them.
To prep, take the work you did in data modeling and practice how you'd load and transform the data from the logging sources into your target tables using SQL. Once that's done, spend some time thinking about how you could help improve the efficiency and scalability for processing for data so massive.
For the SQL portion, you should prioritize practical SQL analytics over stuff like advanced window functions. Concentrate on queries that calculate key metrics like tracking repeat users or churn rates.
Coming down to the coding section, make sure that you know all the data structures and how to manipulate them well. You've got to be familiar with strings, set operations, etc. in your programming in Python, and make sure you understand how dictionaries, lists and loops work.
Candidates say data arrives piece by piece, and you’re supposed to write code that produces the expected output as it arrives. One candidate even reported getting a Python question and an SQL question on the same scenario with different outputs, covering batch on one side and stream on the other.
You can even find an overview of the internal tech stack that DEs use at Meta in this article.
A Reddit user sharing the difficulty of Python/SQL problems and how they were short on time during the Meta DE interview, 2025
By now you would’ve understood that Meta seeks to see breadth over depth so the interviewer needs to see consistent signal across all the focus areas and the time crunch is definitely a beast of its own. If you’re gunning for Meta hard and want leverage over other candidates in the pool, we suggest that you sit down 1:1 with our experts to practice this format and get some personalized prep plans and feedback.
Nobody better to tell you how to prepare than a Meta DE who sat through the same interview and now conducts it. They have their hand on the pulse for what kind of candidates are being favored right now and that can meaningfully change the shape of your prep.
Fill out a quick form to get matched with an expert or book directly with a coach of your preference.
Sample questions for the Full Stack onsite round:
- Prepare a design model for a gaming company such as Epic Games.
- Design ETL pipelines for the above model.
- Write SQL queries for the above design model.
- Design a database for an app such as Google Classroom.
- Design a relational database for Uber.
See the full list of Meta DE onsite questions grouped by focus area in our free question bank that’s built by vetted reports from candidates and interviewers.
Generally speaking, keep checking in with your interviewer by asking them, “Would you like me to keep going?”, or “Is this the direction you want me to take?”. Ask clarifying questions before you commit to a design, because a majority of the missteps we see in post mortems, esp in the modeling segment, trace back to a design that was committed hastily and defended late.
2. Ownership interview
This is a 30 minute behavioral interview that will test your initiative. It's a term used to gauge how well you can influence your peers and stakeholders in their cross-functional roles. You'll have to rely on your past experiences that demonstrate this skill to answer 5-6 questions with follow ups.
This round can be scheduled before or after your technical rounds.
Officially, Meta's internal PDF lists 5 things for the interviewer to assess, and they're all specific enough for you to prepare a story against in advance. They’re looking for evidence that you can:
- Execute tasks and manage projects of increasing complexity and ownership
- Work independently while also collaborating effectively
- Communicate clearly and are (truly) open to feedback
- Use data and rationale to influence decisions and convince teams
- Grow as a leader and mentor with time
Expect questions like:
- What do you do if someone from a different org challenges your approach?
- Tell me about a time you used data to clarify something when everybody else was working off instincts.
- What has been your most complex project so far?
To have an anecdote for every type of scenario or follow up, we suggest that you make a story bank. Take the classic STAR framework and build it out into STAR+ (Situation, Task, Action, Result + the X-Factor: Learning) to structure your stories. The X-Factor at the end - Learning - is your definitive chance to demonstrate intellectual humility, and helps you reflect on a personal takeaway on how that situation changed you for the better.
You should steer clear of any technical jargon in this round, even if you're talking about quantifiable metrics.
Hiring Committee Review & Team Match
At the end of each round, interviewers get 24 hours to submit their notes and pick between Strong Hire, Hire, Leaning No Hire, or No Hire for you.
The Hiring Committee, consisting of senior DEs then reviews that feedback to make the final call and decide your level.
If the committee signs off on a “yes”, you’ll then move into the Team Matching phase where you'll do 30-minute informal chats with HMs until you zero in on a fit.