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Meta Data Engineer Interview Guide 2026
Complete Meta Data Engineer interview guide. Learn the full-stack DE loop covering SQL, Python, data modeling, and product sense, plus preparation tips and compensation by level.
5 min read
Updated Sep 2026
1050+ practice questions
1050+
Practice Questions6
Rounds6
Categories5 min
ReadTL;DR
Meta runs one of the most standardized Data Engineer loops in the industry. After a recruiter screen, you face a technical screen that mixes SQL and Python in a single session, then a virtual onsite with four rounds: SQL, Python coding, data modeling, and a combined product sense and behavioral round. Every technical round is grounded in realistic product data, such as events tables for posts, likes, and messages. The bar is speed and fluency rather than algorithmic cleverness. You should be able to write windowed SQL and manipulate dictionaries and lists in Python without hesitation, design a star schema for a product area, and define metrics that capture whether a feature is working. Most candidates hear back within a week of the onsite, and the whole process takes 4 to 8 weeks.
4-8 weeks
1050+ questions
Sample Questions
1050+ in practice bank
Compute 7-day rolling active users from a login events table
Given a table of user login events, write SQL to compute the rolling 7-day distinct active user count for each day. Tests window frames and distinct counting strategies.
Sessionize raw click events in Python
Given a list of (user_id, timestamp) tuples, group events into sessions separated by 30 minutes of inactivity and return session counts per user, in pure Python.
Top 3 posts per group by engagement
Write SQL using ROW_NUMBER to return the three most engaged posts per group per day from an engagement events table.
Design the data model for Facebook Marketplace
Define fact and dimension tables to support listing, messaging, and transaction analytics. State the grain of each fact table and discuss how to handle listing state changes.
Define success metrics for Reels sharing
Propose a north star metric and guardrail metrics for a new share flow in Reels, and explain how you would detect cannibalization of other sharing surfaces.
Tell me about a data quality issue you caught in production
Meta DEs own dataset quality. Describe detection, root cause, communication to consumers of the data, and the prevention you put in place.
About the Interview Process
Meta's Data Engineer loop is highly consistent across teams because hiring is centralized and team matching happens after you pass. Every round uses product-flavored data problems rather than abstract puzzles. Interviewers score signal in four areas, SQL, Python, data modeling, and product sense, and the hiring committee wants positive signal in all of them.
Recruiter Screen
Background conversation covering your experience with SQL, Python, and pipelines. The recruiter explains the loop structure and shares preparation material. Meta recruiters are unusually transparent about what each round tests, so ask directly.
Technical Screen
One interviewer, split roughly evenly between SQL and Python. Expect around four short SQL questions on one schema and around four Python questions manipulating basic data structures. Speed matters, most candidates who fail simply run out of time.
Onsite: SQL
Two or three SQL problems against a product schema, such as daily active users, retention cohorts, or top-N per group with window functions. Clean, correct SQL written quickly beats clever SQL written slowly.
Onsite: Python
Data manipulation in pure Python, such as aggregating event logs, joining two lists of dictionaries, or computing sessionized metrics. Not LeetCode-style algorithms, but you must be fluent with dictionaries, sorting, and edge cases.
Onsite: Data Modeling
Design the warehouse tables for a Meta product, for example Messenger or Ads reporting. You will define fact and dimension tables, choose grains, handle slowly changing dimensions, and then write a few queries against your own model.
Onsite: Product Sense & Behavioral
Half metric definition and product judgment, half behavioral. You might be asked how to measure the health of Groups, then asked about a time you disagreed with a data scientist about a metric definition.
Timeline
4 to 8 weeks from recruiter screen to offer. The technical screen is usually scheduled within two weeks, and the onsite follows two to three weeks later.
Tips
Use the free question bank format to your advantage, practice answering two SQL questions in 40 minutes total.
When modeling, name your fact and dimension tables explicitly and state the grain of every fact table out loud.
Bring two or three strong behavioral stories about pipeline ownership, conflicting stakeholder requests, and a data quality incident.
If you are stronger in analytics than engineering, say so early. The DE role at Meta sits between analytics and infrastructure, and interviewers calibrate to the role.
What they test
Meta's DE loop is a fluency test across the four skills a production analytics engineer uses daily. The SQL rounds live in window functions, conditional aggregation, and joins over event tables. The Python rounds test whether you can transform raw data structures without a framework doing the work for you.
Data modeling is the round that most often separates senior from junior candidates. Interviewers want a clearly stated grain for each fact table, sensible dimension design, and awareness of trade-offs such as pre-aggregation versus flexibility. Product sense is graded on whether your metrics connect to a user or business goal, not on inventing exotic metrics.
How to prepare in four weeks
Spend week one entirely on SQL speed work with window functions. Week two is pure Python data manipulation, doing everything you would normally do in pandas using plain dictionaries and lists. Week three is data modeling, design schemas for five familiar products and write queries against them. Week four is product sense and behavioral prep, define launch metrics for recent Meta features and rehearse stories in a structure the interviewer can follow.
Leveling & Compensation
| Level | Title | YoE | Total Comp (USD/yr) |
|---|---|---|---|
IC4 | Data Engineer | 1-3 yrs | $170k - $260k |
IC5 | Data Engineer | 3-6 yrs | $240k - $380k |
IC6 | Senior Data Engineer | 6+ yrs | $330k - $540k |
Data Engineer
Executes well-scoped pipeline and dataset work independently. Fluent in SQL and Python, writes tested ETL, and partners with a data scientist on metric definitions.
Data Engineer
Owns the data architecture for a product area. Designs models other teams build on, drives data quality standards, and influences product decisions with data.
Senior Data Engineer
Sets data strategy across multiple teams. Leads large migrations and platform adoption, mentors other DEs, and is the recognized authority on their domain's data.
How to Stand Out
Behavioral Focus Areas
Impact orientation: choosing work that moves metrics, not just work that is interesting
Ownership: running your pipelines and datasets end to end, including quality and documentation
Cross-functional partnership: working daily with data scientists, product managers, and software engineers
Moving fast: making pragmatic trade-offs and shipping incrementally
Communication: explaining data caveats and metric definitions to non-technical partners
1.
Drill SQL window functions until they are automatic. Nearly every Meta DE SQL question involves ROW_NUMBER, LAG, or a rolling aggregate.
2.
Practice Python without pandas. Meta asks for pure Python manipulation of lists and dictionaries, such as building an aggregation from raw event tuples.
3.
For data modeling, learn the classic star schema cold and practice modeling Meta-style products such as Messenger, Reels, or Marketplace.
4.
Always start metric questions with the goal of the feature, then define one north star metric and a small set of guardrails.
5.
Talk while you code. The interviewer grades communication and speed together, and silent candidates run out of time.
6.
Expect two questions per technical round. Budget roughly 20 minutes each and do not get stuck polishing the first.
Related Courses
Recommended Resources
FAQ
Is the Meta DE interview more like analytics or engineering?
It sits in the middle. The loop tests SQL and metric thinking like an analytics interview, but the Python and modeling rounds expect engineering rigor. There is no distributed systems round and no LeetCode-style algorithms round.
Do I need Spark or Airflow experience?
Production experience with pipeline tooling helps your resume and behavioral stories, but the interview itself does not test any specific tool. Everything is plain SQL and plain Python.
How fast do I really need to be?
Fast. Most rounds contain two or more questions and interviewers expect working answers to all of them. If you regularly finish medium SQL questions in under ten minutes, you are in good shape.
What happens after I pass the loop?
You enter team matching, where hiring managers with open headcount reach out. This can take anywhere from days to several weeks depending on your location and level.
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