Meta Data Engineer Mock Interview

Meta's phone screen for DE candidates is almost always SQL. Pure SQL. No Python, no system design. You get one or two problems in a shared coding environment, and the interviewer evaluates correctness, query structure, and whether you can talk through your reasoning while writing. The problems typically involve user engagement data: daily active users, session durations, content interactions. Meta's phone screen SQL is harder than most companies' onsite SQL. They expect window functions, self-joins, and multi-step CTEs as baseline skills, not advanced techniques. If you can't write a running total with a window function without thinking about it, you're not ready for this round. Practice on DataDriven: DataDriven's SQL mock interview mode replicates this exactly. Timed, plain editor, real execution. The AI grader catches common Meta phone screen mistakes: forgetting to handle NULL in joins, using GROUP BY when a window function is needed, and writing correct logic with inefficient query plans.

The onsite SQL round is where Meta separates strong candidates from exceptional ones. You'll get 2 to 3 multi-step SQL problems where each step builds on the last. A typical pattern: 'Here's a table of user interactions. First, calculate daily engagement metrics. Now, using those metrics, identify users whose engagement dropped more than 50% week-over-week. Finally, segment those churning users by their account age and content type preferences.' Each step adds complexity. The interviewer watches how you decompose the problem, whether you reuse intermediate results with CTEs, and how you handle edge cases like users with zero interactions in a given week. Practice on DataDriven: DataDriven's multi-step SQL challenges mirror this format. You write one query, see the results, then get a follow-up that builds on your output. The AI tracks whether your CTEs are structured for reuse and whether your logic holds when the follow-up changes the constraints.

Meta's Python round for DE candidates focuses on data processing, not algorithms. Expect problems like: parse a log file and compute session-level metrics, build an ETL function that transforms nested JSON into flat records, or write a deduplication function that handles conflicting timestamps. Meta cares about production-quality code. Error handling, type hints, meaningful function names, and docstrings all matter. The interviewer will ask follow-up questions about scale: 'This file is now 50GB. How do you change your approach?' They want to hear about generators, chunked processing, and memory management. Practice on DataDriven: DataDriven's Python challenges run your code for real. You can test edge cases, handle file I/O, and see actual output. The AI grader evaluates code structure, edge case coverage, and whether your solution would survive being called on a 50GB file without running out of memory.

Meta's system design round for DEs focuses on data infrastructure for social-scale products. Common prompts: design the data pipeline for News Feed ranking, build a real-time metrics system for Instagram Stories, design the data model and pipeline for Marketplace transactions, or build an anomaly detection system for ad spend. Meta's specific focus areas: data freshness (how fast does new data reach the dashboard?), consistency tradeoffs (is eventual consistency acceptable for this use case?), and the balance between batch and streaming. They want you to discuss specific technologies: Spark, Presto, Kafka, Hive, and their internal equivalents. Practice on DataDriven: DataDriven's discussion mode for system design simulates a Meta interviewer. The AI asks clarifying questions about your freshness SLAs, pushes back when you choose batch processing for a use case that needs sub-minute latency, and scores your answer on requirements gathering, architecture soundness, and tradeoff reasoning.

Meta's behavioral round evaluates culture fit, specifically around Meta's core values: Move Fast, Be Bold, Focus on Impact, Be Open, and Build Social Value. The interviewer asks questions like: 'Tell me about a time you shipped something that wasn't perfect because speed mattered,' 'Describe a project where you had to push back on a stakeholder,' and 'How do you decide what to work on when you have more tasks than time?' Meta values engineers who bias toward action, are comfortable with imperfect information, and can articulate the impact of their work in concrete metrics. Practice on DataDriven: DataDriven's behavioral mode prompts you with Meta-specific scenarios and evaluates your responses for specificity, impact quantification, and alignment with Meta's stated values.

Start with a user_actions table (user_id, action_type, timestamp). Step 1: Calculate daily active users by action type. Step 2: Compute 7-day rolling averages. Step 3: Identify action types where the rolling average dropped more than 20% compared to the prior week. This is Meta's bread and butter. They want to see whether you can build a chain of CTEs where each step produces clean intermediate results. The interviewer will ask you to add a fourth step mid-problem to test your adaptability.

Given a friendships table (user_id_1, user_id_2, created_at) and a user_profiles table, find: mutual friends between two users, users with the most friends-of-friends, or the degree of separation between two users (up to 3 hops). These problems test your ability to work with self-referential data. The key challenge is handling the bidirectional nature of friendships without double-counting. Meta interviewers pay attention to whether you UNION the relationship in both directions upfront or handle it in every subsequent query.

Given a login_events table, calculate Day 1, Day 7, and Day 30 retention for a cohort of users who signed up in January. Then segment by acquisition channel. Then identify which channel has the highest retained-user lifetime value using a separate purchases table. This is a 3-table, 4-step problem that tests your ability to manage complexity. Meta interviewers specifically check whether you handle users who logged in on Day 1 and Day 30 but not Day 7 (they're retained at Day 30 but churned at Day 7).

Design the data model and query pattern for News Feed content ranking signals. You have posts, reactions (like, love, angry, etc.), comments, shares, and view durations. Build a scoring query that combines these signals with time decay. Meta's follow-up: 'Now a new reaction type is added. How does your model change?' They're testing whether your schema is extensible or if adding a reaction type requires rewriting queries.