Meta runs the most SQL-intensive data engineer interview of any major tech company. That's not an opinion. It's a pattern you can verify across thousands of interview reports. Their phone screen is pure SQL. Their onsite has a dedicated SQL deep dive round with multi-step problems where each query builds on the last. If you're comfortable with basic SELECT statements but haven't drilled window functions, self-joins, and chained CTEs until they feel automatic, Meta's interview will expose that gap in the first 10 minutes.
DataDriven's Meta mock interview mode is built around this reality. Every SQL problem runs against real data. The multi-step format mirrors Meta's exact interview structure. The AI grader evaluates query correctness, CTE structure, and whether your intermediate results are clean enough to build on. Over 1,000 questions, including hundreds tagged to Meta's specific difficulty level and domain distribution.
Onsite rounds
SQL weight
Common level
Senior DE TC
Meta's data infrastructure is built on SQL. Their internal query engine (Presto, which they created) powers most analytics work across the company. Data engineers at Meta spend 60 to 70% of their time writing, optimizing, and debugging SQL queries against tables with billions of rows. The interview reflects this reality.
At Google, SQL is one of four or five skills being evaluated. At Amazon, Leadership Principles take up a full round. At Meta, SQL dominance is the defining characteristic of the DE interview. The phone screen is SQL-only. The onsite includes a dedicated SQL deep dive that's harder than most companies' entire technical assessment. Even the system design round often loops back to "what would the query look like to serve this use case?"
This means your preparation strategy should be different from what you'd do for Google or Amazon. At Meta, SQL proficiency isn't a box to check. It's the primary signal the interview is designed to measure.
The social graph adds a layer of complexity unique to Meta. Their data model revolves around users connected to other users, and those connections generate events at massive scale. A single user action (posting a photo) triggers data updates across the poster's profile, every friend's News Feed, content recommendation models, ads targeting systems, and safety classifiers. When Meta asks you to write SQL against this data, they're testing whether you can reason about self-referential relationships, handle bidirectional edges without double-counting, and write queries that perform well against tables where a single user might have 5,000 friend connections.
Meta's DE interview has 5 stages: recruiter screen, phone screen (SQL), and 3 to 4 onsite rounds. Here's what happens in each and how to simulate it on DataDriven.
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.
Meta rotates specific questions, but the patterns stay consistent. Master these four and you'll recognize the structure of any Meta SQL problem on sight.
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.
If you're applying to multiple companies, here's how Meta's loop differs. This affects how you allocate your prep time.
Meta
Highest of any FAANG. Multi-step problems with 3 to 4 sequential queries building on each other. Window functions and self-joins treated as basics.
Google / Amazon / Netflix
Google and Amazon ask hard SQL but rarely chain 3+ steps. Netflix leans more toward system design. Apple's SQL rounds are shorter.
Meta
Data freshness and consistency tradeoffs. Real-time vs. batch. Social-scale data (billions of edges in the social graph). Specific to Meta's product suite.
Google / Amazon / Netflix
Google emphasizes monitoring and GCP stack. Amazon focuses on reliability and AWS services. Netflix cares about streaming architecture (Kafka, Flink).
Meta
Meta's core values: Move Fast, Be Bold, Focus on Impact. They want quantified impact ('reduced pipeline latency by 40%' not 'improved performance').
Google / Amazon / Netflix
Amazon's Leadership Principles are the most structured. Google's Googleyness is the most subjective. Netflix evaluates culture fit through the take-home and onsite discussions.
Meta
Fast. Phone screen to offer in 3 to 5 weeks. Meta's recruiting team moves quickly and often has multiple teams competing for the same candidate.
Google / Amazon / Netflix
Google is slowest (4 to 8 weeks, sometimes longer due to hiring committee). Amazon is moderate (3 to 6 weeks). Netflix varies by team.
Every Meta system design round includes some version of this question: "How fresh does this data need to be, and what are you willing to sacrifice for that freshness?" It's the defining tradeoff in Meta's data architecture.
News Feed needs data that's minutes old. If a friend posts a photo, the ranking model needs to consider it within 2 to 5 minutes. But the engagement metrics used to rank that photo (views, likes, comments) are updated by batch pipelines that run hourly. So the ranking system uses a hybrid: streaming for the existence of new content, batch for the engagement signals. The interviewer wants to hear you articulate this tradeoff, not just pick one.
Ads data is even more constrained. An advertiser's budget needs to be checked in near-real-time (sub-second) to avoid overspending. But the click attribution pipeline runs with a 24-hour window to account for delayed conversions. The ad serving system and the analytics system run on different freshness contracts against the same underlying data. Meta's DE interviewers test whether you understand why these two systems can't use the same pipeline, even though they share a data source.
A/B test analysis requires strict consistency. If an experiment is evaluating the impact of a new ranking algorithm, the metrics pipeline must ensure that the treatment and control groups are computed from the same data snapshot. Eventual consistency is not acceptable here because even small timing differences between treatment and control can produce misleading results. Meta interviewers probe whether you know when eventual consistency is fine (News Feed ranking) versus when it's dangerous (experiment analysis).
DataDriven's system design practice mode includes freshness-vs-consistency scenarios drawn directly from these patterns. The AI interviewer will challenge your assumptions and push you toward the specific tradeoffs Meta cares about.
Meta's process moves fast. From phone screen to offer can be 3 to 5 weeks. Start this plan before you schedule the phone screen.
Meta's interview is SQL-dominant. Spend 70% of your prep time on SQL for the first two weeks. Do 5 timed SQL problems per day, focusing on: window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running sums), self-joins for social graph queries, multi-step CTEs, date arithmetic, and NULL handling in outer joins. Use DataDriven's SQL problems filtered to 'Meta difficulty' for the most accurate practice.
Split time 50/50 between Python data processing and system design. For Python: practice parsing nested JSON, building ETL functions, chunked file processing, and deduplication logic. For system design: study Meta's architecture papers (they publish extensively). Practice drawing pipelines for real-time analytics, content ranking data flows, and event-driven architectures. Learn the trade-offs between Spark (batch) and Flink (streaming) for social-scale data.
Run 3 full mock interviews using DataDriven. Each mock should include: 1 SQL round (45 min, 2 to 3 multi-step problems), 1 system design round (45 min), and 1 behavioral round (30 min). Review your results after each mock. Identify your weakest SQL pattern and drill it with 10 extra problems. Time yourself strictly. Meta interviewers do not give extra time.
Review your behavioral stories. Make sure each one has: a specific situation, a concrete action you took (not the team), a quantified result, and a connection to one of Meta's core values. Re-read your weakest SQL problems. Get 8 hours of sleep the night before. Stamina matters when you have 4 rounds in a single day.
SQL-intensive questions at Meta's actual difficulty level. Real code execution. AI grading that catches the mistakes Meta interviewers penalize.