INTERVIEW GUIDE

Meta Data Engineer Interview: Questions & Process

Meta's data engineer interview is heavy on SQL and data modeling, with a strong product-analytics flavor. Expect a recruiter screen, a technical screen with SQL and Python data manipulation, and a full loop covering complex SQL, dimensional/ETL data modeling, an analytics/product-sense round, and behavioral. They want engineers who can turn ambiguous product questions into clean, scalable pipelines and the right metrics.

The interview process

1. Recruiter screen — ~30 min call

Tests: background, why Meta, role expectations, logistics

2. Technical phone screen — 45-60 min live coding

Tests: SQL queries plus Python data manipulation against a realistic dataset

3. SQL / data manipulation onsite — 45 min

Tests: complex joins, window functions, aggregation, and optimizing queries

4. Data modeling / ETL design — 45 min

Tests: designing fact and dimension tables, schema tradeoffs, and a pipeline for a product feature

5. Analytics / product sense + behavioral — 45 min each

Tests: defining metrics for a product, diagnosing a metric drop, and Meta's core values (move fast, impact)

Questions you're likely to get

Technical

Write a query to find the top three earners in each department.
Using a window function, compute each user's day-N retention from a logins table.
Find employees who earn more than their direct manager.
Calculate the daily delivery rate where the order date equals the delivery date.
Given a temperatures table, find days warmer than the day before.
Join two tables to combine person and address data, keeping people with no address.

Role-specific

Design a data model to track likes, comments, and shares for a feed product.
How would you build an ETL pipeline that aggregates daily active users at scale?
Walk me through how you'd handle late-arriving and duplicate events in a pipeline.
Daily active users dropped 5% overnight — how do you investigate whether it's real or a data issue?
How do you decide between a star schema and a wider denormalized table for an analytics use case?

Behavioral

Tell me about a time you delivered a data project under ambiguity.
Describe a time you found and fixed a data quality issue that others relied on.
Tell me about a time you influenced a decision with data.

Practice these problems live

Relevant LeetCode problems for the Meta Data Engineer loop. Start a live, AI-run coding interview on any of them — or open the problem on LeetCode.

▶ Department Top Three Salaries· Hard · LeetCode #185

Department top three salaries tests the window/rank patterns central to Meta's SQL rounds.

▶ Game Play Analysis IV· Medium · LeetCode #550

Game play analysis IV mirrors the retention metrics Meta data engineers compute constantly.

▶ Immediate Food Delivery II· Medium · LeetCode #1174

Immediate food delivery II tests conditional aggregation and ratio metrics.

▶ Employees Earning More Than Their Managers· Easy · LeetCode #181

Employees earning more than managers checks self-join fundamentals.

▶ Rising Temperature· Easy · LeetCode #197

Rising temperature tests date-based self-joins common in event analysis.

▶ Combine Two Tables· Easy · LeetCode #175

Combine two tables verifies left-join basics that underpin pipeline joins.

How to answer (worked examples)

Design a data model to track likes, comments, and shares for a feed product.

Start by clarifying the analytics questions the model must answer, then propose a dimensional design: a fact table for engagement events with foreign keys to user, content, and date dimensions. Talk about grain (one row per event), partitioning by date for scale, and how you'd pre-aggregate for fast dashboards. Volunteer tradeoffs — normalized vs. denormalized, storage vs. query speed. Meta wants modeling that scales and serves real product questions.

Daily active users dropped 5% overnight — how do you investigate?

Be structured: first confirm it's not a pipeline or logging bug (check data freshness, job failures, schema changes), then segment the drop by platform, region, app version, and new vs. returning users to localize it. State a hypothesis, test it against the data, and separate 'real behavior change' from 'instrumentation artifact.' This blends data-engineering rigor with product sense, exactly what the analytics round tests.

Tell me about a time you found and fixed a data quality issue.

STAR. Situation: who depended on the data and what was wrong. Action: how you detected it, traced the root cause upstream, fixed it, and added a check to prevent recurrence. Result: the impact and the safeguard you left behind. Meta values engineers who treat data trustworthiness as ownership, not someone else's job.

What Meta looks for

Strong SQL — comfortable with window functions, complex joins, and optimization
Solid dimensional modeling and pipeline design at scale
Product sense — you connect data work to the metrics that matter
Data quality ownership and rigor about correctness
Clear communication of tradeoffs and assumptions
Red flag: writing queries that work on small data but ignore scale or partitioning

FAQ

How much SQL does the Meta data engineer interview involve?

A lot. SQL is the core skill tested, including window functions, multi-table joins, and aggregation. Expect at least one dedicated SQL round plus SQL in the screen.

Do I need Python too?

Yes, for data manipulation. You won't face hard algorithm puzzles, but you should comfortably reshape and clean data in Python (or a pandas-like approach).

Is the role more analytics or pure engineering?

Meta data engineering blends both. The data modeling and product-analytics rounds mean you should be able to define metrics and reason about product behavior, not just build pipelines.

What should I focus on for data modeling?

Dimensional modeling — fact and dimension tables, grain, slowly changing dimensions, and tradeoffs between normalized and denormalized designs for analytics at scale.

How long is the process?

Typically a few weeks from screen to onsite to decision, though it varies with scheduling and team demand.

Walk into the Meta loop fluent in the questions

Writing SQL on a screen is one thing; explaining your data model and metric reasoning out loud is another. Practice this exact interview — the SQL, the modeling, the product-sense round — with OfferLoop's realtime voice coach before the real thing.

Practice this interview out loud →

All Meta interviews →Meta Software Engineer interview →Meta Data Analyst interview →Meta Data Scientist interview →Meta Machine Learning Engineer interview →

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Interview formats vary by team, level and year, and this guide is compiled from general knowledge of publicly discussed hiring processes — treat it as preparation material, not an official description of Meta's current process.