You're looking at one of the most SQL-heavy loops in big tech, and the good news is it's more predictable than it looks. Meta tells you what they're testing if you know where to look. You'll see six rounds, two of them SQL-forward, and the rubric rewards clarity over cleverness. This guide walks you through the loop one stage at a time, shows you five questions in the real Meta voice, and gives you a four to six week plan you can actually follow.
Rounds in loop
Questions are SQL
Use GROUP BY
Typical prep
Source: DataDriven analysis of 1,042 verified data engineering interview rounds.
You'll move through six stages from recruiter ping to offer, and each interviewer writes their notes without seeing the others. One rough round won't kill you, but you can't hide behind a great round either. Think of it as six independent exams, all graded on the same transcript.
Your first conversation with Meta is a non-technical call. The recruiter wants to understand your background, what attracted you to Meta, and whether your experience lines up with the team and level. They will ask about the scale of data you have worked with, the tools in your stack, and why you want to work at Meta specifically. This is also where you learn about the role: which product area (Ads, Integrity, Instagram, Reality Labs), what the team builds, and what the day-to-day looks like. Recruiters are calibrated on level expectations, so how you describe your past work signals whether you are IC3, IC4, or IC5.
A live SQL coding round, typically 1 to 2 problems. The interviewer shares a document or collaborative editor, describes a schema (usually Meta-like: user sessions, ad impressions, content interactions), and asks you to write queries. Problems emphasize aggregation, window functions, and multi-step logic. The interviewer watches your thought process as much as your final answer. They want to see you clarify requirements, handle edge cases unprompted, and think about what could go wrong with real data. Finishing early often means a follow-up question layered on top of your solution.
Harder than the phone screen. You will face 2 to 3 SQL problems with increasing complexity. The first problem is a warm-up: basic aggregation or a straightforward join. The second adds window functions, date arithmetic, or multi-step CTEs. The third may involve query optimization: your solution works, now explain how to make it efficient on a table with 500 billion rows. This round separates candidates who memorize patterns from those who understand how SQL engines process queries. The interviewer might ask about indexing, partitioning, or why a particular join order matters.
Not every Meta DE loop includes this round, but it is increasingly common. The focus is practical data manipulation, not LeetCode algorithms. Expect tasks like parsing JSON logs, transforming nested data structures, building a simple ETL function, or writing a data validation pipeline. The interviewer cares about clean code, error handling, and whether you think about edge cases that would cause a production pipeline to fail at 3 AM. Some teams test basic object-oriented design or ask you to write tests for code you just wrote.
Design a data pipeline or data platform component at Meta scale. Examples include real-time ad click analytics, cross-platform user activity aggregation, content moderation event processing, or a data quality monitoring system. The interviewer cares about your ability to reason at scale (billions of events per day), handle failure gracefully, and make deliberate tradeoffs between latency, cost, and complexity. You are expected to drive the conversation: gather requirements, sketch architecture, discuss component choices, and address failure modes without being prompted.
Meta calls this the values round. Questions focus on collaboration, conflict resolution, impact, and operating under ambiguity. They want specific examples from your past work, structured in STAR format (Situation, Task, Action, Result). Meta values 'Move Fast' and 'Build Social Value,' so your stories should demonstrate delivering quickly, iterating based on feedback, and caring about the people who use your data products. This round carries real weight. Strong technical candidates get rejected when their behavioral answers are vague or rehearsed-sounding.
Meta interviews are not just about getting the right answer. Each round evaluates a different dimension of your engineering ability.
Can you write correct, readable queries under time pressure without autocomplete? Do you handle NULLs, duplicates, and edge cases without being prompted?
Do you think about performance at Meta's scale? When you write a query or design a pipeline, do you mention partitioning, indexing, and data volumes?
Can you explain your thought process clearly while solving a problem? Do you ask good clarifying questions before diving into code?
Can you design end-to-end data systems? Do you consider failure modes, data quality, monitoring, and the needs of downstream consumers?
Can you design schemas that are both analytically useful and performant? Do you understand grain, slowly changing dimensions, and the tradeoffs of different modeling approaches?
Can you demonstrate specific examples of working across teams, resolving disagreements, and delivering impact through others? Do your stories have measurable outcomes?
These questions reflect the style, difficulty, and domain context of actual Meta DE interviews. Each includes a detailed approach the interviewer expects.
Filter to posts within the last 30 days. Use the date-minus-ROW_NUMBER technique to create groups of consecutive days per user. Count the size of each group and filter where the count is 5 or more. Return distinct user IDs. The interviewer will check whether you handle users who posted multiple times on the same day (need DISTINCT dates first). Discuss performance: partitioning by user_id and ordering by post_date makes the window function efficient.
Find each user's first session date. Join back to the sessions table to check for activity exactly 7 days later. Group by cohort date (the first session date) and compute the ratio of retained users to total users per cohort. Key details: define 'active' clearly (any session event counts?), handle timezone differences, and discuss whether you use a calendar table to fill in cohort dates with zero retention. The interviewer wants to see you think about what retention means before writing SQL.
Parse each record, validate that user_id and timestamp fields exist, skip or log malformed records. Use a defaultdict(list) to group by user_id. Sort each user's list by timestamp. The interviewer looks for error handling (what if timestamp is not parseable?), memory awareness (what if there are 100M records?), and clean code structure. Mention that in production, you would stream records rather than loading all into memory.
Ingestion: ad events (impressions, clicks, conversions) flow into Kafka topics partitioned by campaign_id. Processing: a Flink job computes windowed aggregates (CTR, spend, conversions) in 60-second tumbling windows. Serving: write aggregates to a low-latency store (Redis or Cassandra) with campaign_id as the key. Dashboard queries the serving layer. Discuss backpressure if event volume spikes, late-arriving events and watermarks, and how to handle the initial backfill when a new campaign starts. Address data quality: what if click events arrive before impression events?
Structure with STAR. Describe the situation: a stakeholder wanted X, explain why it was problematic (data did not exist, the metric definition was flawed, the timeline was unrealistic). Describe how you communicated the issue: not just saying no, but proposing an alternative. Show the result: the stakeholder got what they actually needed, trust increased, and you avoided building something that would have been thrown away. The interviewer wants to see that you can be diplomatically firm and that you think about the downstream impact of data decisions.
A structured plan that covers every skill Meta tests. Adjust the pace based on your starting point, but do not skip any phase entirely.
Focus on window functions, CTEs, and multi-step aggregation problems. Do 3 to 5 problems per day, timed to 20 minutes each. Write SQL in a plain text editor without autocomplete. Practice reading a schema and immediately asking clarifying questions about the data. By the end of week 2, you should be able to solve a medium-difficulty SQL problem in under 15 minutes.
Drill dictionary operations, file I/O (JSON and CSV parsing), and list comprehensions. Write small ETL functions that take raw data and return clean, structured output. Practice error handling: what happens when a field is missing, a value is NULL, or the data type is wrong. Build a habit of writing helper functions instead of monolithic scripts.
Design star schemas for 5 Meta products: News Feed, Marketplace, Reels, Events, and Groups. For each, define fact tables, dimension tables, and grain. Practice explaining your schema out loud, justifying every design choice. Know SCD Type 2 inside and out. Be able to sketch a schema in under 10 minutes and answer three follow-up questions about it.
Study 3 to 4 common DE system design problems: real-time event processing, data warehouse ETL, data quality monitoring, and CDC pipelines. For each, practice drawing architecture diagrams, estimating data volumes, and discussing failure modes. Time yourself to 35 minutes per problem, leaving 10 minutes for the interviewer's questions.
Write out 5 STAR stories covering collaboration, conflict, impact, speed, and technical leadership. Practice telling each story in under 3 minutes. Do at least 2 full mock interviews (one SQL-focused, one system design) with a friend or paid service. Record yourself and review for verbal fillers, unclear explanations, and missed edge cases.
What makes preparing for Meta different from other companies.
Every answer at Meta should acknowledge the scale of their data. When you design a pipeline, mention billions of events per day. When you write SQL, discuss how your query performs on tables with hundreds of billions of rows. When you model data, explain how the schema supports queries across petabytes. Candidates who think small get filtered out. This does not mean you need to have worked at Meta's scale, but you need to demonstrate you have thought about what changes when data grows by 1000x.
Meta built Presto (now the open-source Trino project) for interactive SQL at scale. They use Spark for large batch processing, Scuba for real-time analytics with sub-second query latency, and custom orchestration systems that predate Airflow. You do not need deep knowledge of these tools, but referencing them shows you did your homework. Saying 'I would use Presto for the interactive queries and Spark for the heavy batch jobs' is much stronger than 'I would use some SQL engine.'
Meta is deeply metrics-driven. Data engineers support A/B testing infrastructure, metric computation pipelines, and experiment analysis frameworks. If you can weave experimentation into your answers (e.g., 'This pipeline would need to support slicing metrics by experiment variant'), you demonstrate understanding of how data engineering fits into Meta's product development cycle. Many candidates focus on building pipelines without thinking about what the pipelines are for.
Some candidates spend 95% of their prep time on technical rounds and wing the behavioral interview. At Meta, the values round carries real weight and can be the tiebreaker between two technically strong candidates. Your stories need to be specific, quantified, and structured. 'I worked with the team to fix the issue' is too vague. 'I identified the root cause in the Spark job, proposed a fix, coordinated the rollout with the ML team, and reduced processing time from 6 hours to 40 minutes' is what they want to hear.
Meta interviewers appreciate candidates who understand the products they would be supporting. If you are interviewing for the Ads team, know how the ad auction works at a high level. If you are interviewing for Integrity, understand content moderation at scale. This context makes your system design answers more grounded and your data modeling schemas more realistic. Spend 30 minutes reading Meta's engineering blog posts for the team you are interviewing with.
You'll do your best thinking when the clock stops scaring you. Start practicing at interview pace now, and by Week 4 you won't notice it.
Practice Meta-Level SQL