Meta Data Engineer Interview Guide

Meta's data engineer (DE) interview covers more ground than most data engineering loops. The technical screen has the highest pass bar candidates report in the process, and the onsite blends product sense, data modeling, SQL, and Python into every round, a format built to test range.

This guide breaks down each stage of the Meta data engineer interview, what interviewers look for, and how to prepare with real example questions, actionable tips, and resources.

Meta data engineer interview process

The Meta DE interview moves through three stages over roughly 3-5 weeks: a recruiter screen, a technical screen, and an onsite loop of four technical rounds plus a behavioral round.

The loop is tightly standardized across teams, and the distinctive pressure is density. The technical screen packs 10 questions into a single hour, and every onsite round layers four skill areas on top of each other.

Here's what the process typically looks like:

1. Recruiter screen: Background, motivation, and alignment with the role, plus a prep handoff with Meta's official resources
2. Technical screen: Five SQL and five Python questions in CoderPad, with a pass bar of three correct in each half
3. Onsite loop: Four blended technical rounds covering product sense, data modeling, SQL, and Python, plus a standalone behavioral round

Recruiter screen

Meta's data engineer recruiter screen is a 30-minute call focused on fit, motivation, and logistics. What sets it apart from most FAANG recruiter calls is the prep handoff: recruiters share official Meta resources and a structured overview of every round before you reach the technical screen.

Interviewers look for:

• Clarity on your recent work: Whether you can walk through your current role and a recent project in under five minutes without losing structure
• Motivation for Meta: Why Meta specifically, grounded in product, scale, or team reasons rather than brand
• Role alignment: Whether your experience maps to the DE competencies Meta tests in the loop
• Compensation fit: Whether your expectations fall within Meta's range for the level they're assessing you at
• Communication pace: How efficiently you answer, since the rest of the loop is built for speed

Recently asked questions

Here are real, recent interview questions reported by candidates:

• Tell me about yourself.
• Walk me through your experience at your current company.
• Why Meta?
• Why data engineering?

Technical screen

The Meta data engineer technical screen is 60 minutes in CoderPad and covers both SQL and Python, with five questions in each half. You need to clear at least three in each language to move to the onsite. It's the tightest pass bar candidates report in the process.

The hour splits cleanly: roughly five minutes of intros, 25 minutes of SQL, 25 minutes of Python, then a short wrap-up. Your interviewer posts each question inline, you solve against a full suite of test cases, and you can ask clarifying questions at any point. Confirm your language preference with your recruiter a few days before the screen; Meta tailors the coding half to the language you select.

Interviewers look for:

• Speed with precision: How quickly you can write working code that passes the full test suite
• Reasoning out loud: Whether you narrate your approach in real time as you work
• Clarifying questions: Whether you confirm input shape, edge cases, and expected output before writing code
• Recovery under pressure: How you respond when a test case fails or a solution isn't coming together in time
• SQL and Python fluency: Whether you can move between the two languages without losing pacing

SQL questions in the technical screen

The SQL half of the technical screen tests whether you can move through five questions in 25 minutes on a PostgreSQL dataset. Expect scenario-based prompts built around familiar schemas like library systems, employee-salary structures, and transaction tables, with logic that escalates quickly across the set.

Come in fluent with:

• Joins across multiple connected tables
• Group by and having
• Subqueries and common table expressions
• Window functions, including LAG and LEAD
• Aggregations and CASE logic

Sample questions

Here are some real interview questions reported by candidates:

• Given a library schema with books, users, and a check-in/checkout table, find each user and the number of books they checked out but didn't return before the due date.
• Given the same schema, find the number of users who checked out books in a specific category, grouped by age.
• Find the number of people who checked out a book on the same day another person returned it.
• From a transaction table, find the sum of total orders and the count of unique customers.
• Find the number of users who called three or more people in the last week.

Python questions in the technical screen

The Python half of the technical screen mirrors the SQL pacing: five questions in 25 minutes, focused on string manipulation, dictionaries, lists, file I/O, and basic error handling. Meta interviewers expect clean, readable code over compact shortcuts, and they expect you to ask about syntax when you need to.

Come in fluent with:

• String and array manipulation
• Dictionary and hash table patterns
• File reading and writing
• Exception handling with try/except
• Basic sorting and list operations

Sample questions

Here are some real interview questions reported by candidates:

• Given a list of strings, find the second letter of the first word in each string and return the character along with its count of occurrences.
• Write a function that reads a data.csv file, processes it, and handles a file-not-found exception by outputting "file not found."
• Given a dictionary of employees with their department and salary, find the second-highest salary in each department.
• Join two lists and sort the result.
• Remove items from a list based on a specific key.

Onsite loop

Meta's DE onsite loop runs four hour-long technical rounds plus a behavioral round, typically completed in a single day.

Each technical round moves through product sense, data modeling, SQL, and Python, with the case study changing from round to round. You might spend the first 10 minutes on product sense, pivot into data modeling for a schema that supports the product case, then close with SQL and Python tied to the same domain.

Product sense and data modeling in the onsite loop

Data modeling carries the most weight in the Meta DE onsite loop, and it's almost always introduced through a product sense prompt. Every technical round opens with a case study tied to a real-feeling product surface, and you spend the first stretch working through how the product functions, what metrics matter, and what user or business questions the data needs to answer before you touch a schema.

Expect prompts built around Meta-adjacent products and familiar consumer apps: an Instagram metric that's dropping, a ride-sharing service like Uber, an ecommerce store, a Reddit-style notification system, or a movie theater ticketing platform. The product case anchors every technical choice that follows. Your schema, relationships, and partitioning choices should all ladder back to the product question the round opened with.

Interviewers look for:

• Product reasoning first: How well you translate a product case into the data questions that need to be answered
• Clarifying questions: Whether you pressure-test ambiguous requirements before committing to a schema
• Schema fluency: How cleanly you define fact tables, dimensions, primary and foreign keys, and many-to-many relationships
• Scale trade-offs: Your ability to reason about partitioning, bucketing, and indexing at volumes that stress a production system
• ETL awareness: Whether you can describe how the data gets into the model, covering ingestion, transformation, and load steps
• Design evolution: How you adapt the model when the interviewer shifts requirements mid-round

Common mistakes to avoid in the Meta data modeling round

• Skipping clarifying questions before committing to a design
• Mixing up primary and foreign keys or leaving relationships implicit
• Forgetting relationship tables for many-to-many joins
• Over-indexing on one layer of the architecture instead of sketching the full pipeline
• Waiting until the end of the round to explain trade-offs instead of narrating them as you design

Recently asked questions

Here are some real interview questions reported by candidates:

• Design a data model for a ride-sharing app like Uber. Walk through the table relationships, primary and foreign keys, and how you'd partition the transactional tables at scale.
• An Instagram metric is dropping. Walk through your root-cause analysis approach, the data model that would support it, and the follow-up investigation.
• Design a data model for an ecommerce store, covering products, orders, customers, and inventory.
• Design a notification system for a Reddit-style app. What does the backend and data model look like?
• Design a movie theater ticketing system. How would you store the data needed to support end-to-end functionality?

SQL and Python in the onsite loop

SQL and Python show up again inside every blended onsite round, but the prompts are more open-ended than in the technical screen. You solve fewer questions per round because they're embedded in the product and data modeling case study, so SQL and Python have business context attached and a narrower set of correct answers.

Expect SQL questions that push into analytical territory: funnel-style metrics, user-segment breakdowns, and queries that have to scale against a production-sized dataset.

Expect Python questions that move beyond single-function puzzles into data processing, streaming patterns, and logic that ties back to the round's product case.

Interviewers look for:

• Business reasoning in the query: How well your SQL translates a product question into the right metric, filter, or grouping
• Scale awareness: Whether you consider indexing, filtering order, and query cost when the dataset gets large
• Clean Python structure: How readable and testable your code is, especially when logic gets embedded in a larger data flow
• Streaming and windowing intuition: Whether you can reason about time-bounded data in tumbling or sliding windows
• Debugging out loud: How you respond when the interviewer pushes back on correctness or efficiency mid-solution

Recently asked questions

Here are some real interview questions reported by candidates:

• Given a stream of ride requests for a service like Uber, compute the number of ride requests in each 15-minute tumbling window.
• Calculate what percentage of Messenger users who were active yesterday made a video call.
• Write a function that dynamically formats a SQL query based on input parameters.

Behavioral interview

The Meta DE behavioral interview, known internally as the Ownership round, is a 30-minute standalone onsite session outside the blended technical rounds. It tests how you operate as a senior engineer: how you own outcomes, navigate friction, and pull impact out of ambiguous situations.

Meta interviewers are listening for structured, outcome-driven stories. They want to hear how you defined the problem, what you did, and what measurably changed because of it. Prepare stories that hold up when interviewers dig in, since they'll press on your specific role, the trade-offs you weighed, and how you'd approach the same situation now.

Interviewers look for:

• Retrospective clarity: How precisely you describe your role in past outcomes and what you'd change
• Cross-functional impact: Whether your stories show work that moved partners, teams, or product outcomes
• Handling friction: How you navigate disagreement with managers, peers, or stakeholders without losing momentum
• Learning speed: How quickly you pick up unfamiliar tools, systems, or domains and turn them into results
• Communication discipline: How tightly you pace a story, since behavioral rounds move quickly through multiple prompts

Recently asked questions

Here are real, recent interview questions reported by candidates:

• Tell me about a time you led a project end-to-end.
• Tell me about a time you disagreed with your manager or team lead and how you resolved it.
• Tell me about a process you improved that had a measurable business impact.
• Tell me about a time you had to learn a new tool or system quickly and deliver results.

How to prepare for the Meta data engineer interview

1. Practice the 5+5 screen under real time pressure: Set a 25-minute timer for five SQL questions and another 25 minutes for five Python questions. The pass bar is three correct in each half, so your goal is consistent accuracy at that pace, not peak performance on one question. Use Exponent's SQL interview course and medium-difficulty coding practice to build the rhythm.
2. Clarify before you code: Read the prompt out loud, restate it in your own words, and confirm input shape, edge cases, and expected output before typing anything. Meta interviewers expect this and will answer syntax and scope questions directly if you ask.
3. Start with a working approach, then optimize: Get a correct solution on the board first, even if it's the obvious one. Then walk through what's expensive, what you'd index or partition, and where the approach breaks at scale.
4. Study data modeling through real product lenses: Work through dimensional modeling using live consumer products as scenarios, covering fact tables, dimensions, primary and foreign keys, and many-to-many relationships. Practice adapting the schema when requirements shift mid-design.
5. Let product sense frame every data modeling prompt: Before you draw a single table, work through what the product does, what metrics define success, and what questions the data needs to answer. The schema follows from the product case, so skipping this step breaks the round.
6. Structure your behavioral stories for speed: Keep each story under five minutes by leading with the situation, walking through your specific actions, and closing with a measurable outcome. Going over five minutes costs you question volume, which shrinks the round's evaluation surface.
7. Run timed mock interviews in the same formats you'll face: Practice with SQL mock interviews and Meta-specific coaching sessions to get used to solving and explaining under pressure.

Additional resources

• Meta interview question bank
• Meta company hub on Exponent
• Top SQL data engineering interview questions
• Data engineering interview course
• Meta DE onsite prep guide
• Meta DE technical screen prep guide

FAQs about the Meta data engineer interview

How much does a Meta data engineer make?

Here are the reported compensation ranges by level for Meta data engineers, according to levels.fyi:

• IC3: ~$168K
• IC4: ~$226K
• IC5: ~$311K
• IC6: ~$439K

How long does the Meta data engineer interview process take?

The Meta data engineer interview takes roughly 3-5 weeks from recruiter screen to final decision. The structure moves quickly because the loop is standardized, with three clear stages: recruiter screen, technical screen, and onsite loop.

What makes the Meta data engineer interview challenging?

The Meta data engineer interview is challenging because of its density: the technical screen's pass bar is high, and every onsite round blends four skill areas into a single hour. Senior candidates report the time pressure as the most demanding element, since clean execution on individual questions matters less than consistent accuracy across the full set.

What are common mistakes in the Meta data engineer interview?

The most common mistakes in the Meta data engineer interview are skipping clarifying questions before designing or coding, over-engineering SQL when a simpler query works, and ignoring scale trade-offs in data modeling rounds.

Other recurring issues include narrating only at the end of a solution rather than as you work, mixing up primary and foreign keys in schema design, and going over the five-minute behavioral cap and burning question budget.