Meta Data Engineer Interview: The Full Process, Decoded

If you've done a SWE loop before, throw away those expectations. The Meta data engineer interview is shorter, but the surface area is wider. You will write SQL, design schemas, and discuss product sense in the same 60-minute session. And the phone screen has a hard numeric pass bar that will catch you off guard if you don't know it's coming.

This guide covers the full process: every stage, what each one actually tests, and what trips up even strong candidates.

Who This Loop Is For

The Meta data engineer interview (formerly Facebook data engineer interview) runs through the Data Infrastructure and Analytics Engineering org. These are not analysts who fire off SELECT statements and call it a pipeline.

They build the systems that move petabytes of data daily across products used by billions of people. The interview reflects that scope. Which means the bar is real, the questions are open-ended, and knowing window functions is table stakes, not a differentiator.

Drake meme: refusing "a few clicks in Excel once a year", approving "a Python data-pipeline with Polars and a lot of YAML"

This is who you're competing against. Adjust your prep accordingly.

The Loop at a Glance

Stage	Format	Duration	Pass bar
Recruiter screen	Phone call	~25 min	Experience fit
Technical phone screen	CoderPad	60 min	3+ of 5 SQL, 3+ of 5 Python
Onsite: Technical round 1	Case study	60 min	Holistic
Onsite: Technical round 2	Case study	60 min	Holistic
Onsite: Technical round 3	Case study	60 min	Holistic
Onsite: Ownership (behavioral)	Conversation	30 min	Standalone

Four onsite rounds. Three are technical. One is behavioral, and it counts as its own data point in the hiring packet. Not a warmup. Not a free pass.

The Recruiter Screen

Standard 25-minute call. They want to confirm you have real data engineering experience, not BI work with pipeline buzzwords stapled to it. If you have shipped production pipelines, designed schemas at scale, or worked with distributed processing frameworks, say so concretely. The recruiter is checking relevance, not depth.

The Phone Screen Has a Hard Pass Bar

This is where most candidates get surprised. The screen is 60 minutes split into two back-to-back sections: SQL and Python, up to five questions each. Pass bar: three or more correct in each half.

Five SQL questions in 25 minutes averages five minutes each. You cannot write academic, paragraph-length SQL. You have to get to correct, clean, and readable fast. Pacing is the actual test.

SQL questions sit at LeetCode medium. Window functions are non-negotiable: ROW_NUMBER, RANK, LAG, LEAD, NTILE. All of them. Expect aggregation over grouped data, funnel analysis, and deduplication via DISTINCT ON or a CTE with row numbers. A sample prompt: "Find the top 3 most active users per day." That requires partitioning by day, ordering by activity, and filtering to rank 3 or fewer, all in one pass. Under a timer. While being watched.

SELECT user_id, activity_date, activity_count
FROM (
  SELECT
    user_id,
    activity_date,
    activity_count,
    ROW_NUMBER() OVER (PARTITION BY activity_date ORDER BY activity_count DESC) AS rk
  FROM user_activity
) ranked
WHERE rk <= 3;

Python at this stage is algorithm problems, not data processing. Think LeetCode easy-to-medium: string manipulation, arrays, hash maps, basic recursion. The bar is not "implement a streaming processor." It is "can you write clean Python quickly." Pass this round and the Python questions get harder in the onsite. Fair warning.

The Onsite Starts With Product Sense, Not Code

This is the core of the loop, and it works differently than most candidates expect.

Every technical round opens with a product prompt. You might be asked to design a data system for Facebook Groups, analyze engagement metrics for Reels, or model ad attribution. The first 10 minutes are product sense: what does this feature do, who uses it, what questions does the business need the data to answer.

Most data engineering candidates sprint past this. They want to get to the schema. That is a mistake. Product sense is how Meta evaluates judgment. If you do not understand what the data needs to power before you design a schema, your schema will be wrong in ways that are expensive to fix later. And you will be stuck with it.

After product sense, the round pivots into three technical segments that can appear in any order or combination.

Your Schema Is Your Prison. You Built It.

You are given a product case, say video watch events, and asked to design a schema that answers key business questions. This is not normalize-everything academics. Meta wants you to reason about denormalization trade-offs, partitioning strategies, and how the schema supports both real-time queries and batch aggregations.

Common patterns: fact and dimension tables for an events pipeline, slowly changing dimension handling for user attributes, star schemas for reporting. When the interviewer adds a scale constraint mid-round ("this table gets 500 million inserts per day"), they want to see whether you reach for partitioning, bucketing, and approximate aggregations, or whether you treat it as the same problem with a bigger number.

For senior roles (E6 and above), expect the conversation to push into full ecosystem design: how does data get from edge devices into your warehouse, what is the SLA on freshness, how do you detect upstream quality issues before a dashboard breaks.

The Onsite SQL Is Harder Than the Phone Screen

The onsite SQL is tied to the product case from that round's opening discussion. You are not writing a generic retention query. You are writing a 7-day retention query for the specific event schema you just designed.

The SQL and the schema are coupled. If your data model was off, your SQL will fight the schema. Interviewers watch for whether your query reflects understanding of the data or is just a pattern pasted onto an unfamiliar shape.

Fluency with CTEs, window functions, and subquery rewrites is assumed. What separates passing candidates is scale awareness: do you mention indexing, partition pruning, or query cost when the dataset has billions of rows? Do you write a query that will run in seconds, or one that will scan the full table while the interviewer quietly updates their notes?

Pipeline Reasoning, Not Just Code

Onsite Python moves beyond algorithm problems into data processing territory. You might write a function that processes a stream of events with a sliding window, handles late-arriving records, or deduplicates a high-cardinality feed. The code needs to be correct and discussable in terms of production behavior. Not just "it works on my examples."

Airflow and DAG concepts surface here too. Not "write an Airflow DAG from memory," but "your pipeline has 20 downstream jobs and three of them failed. Walk me through how you would debug this." Expect questions about retries, SLA monitoring, backfill strategies for historical data, and what happens when an upstream table is stale.

The Ownership Round Is a Real Signal

This 30-minute standalone round has a name at Meta: the Ownership round.

The questions probe how you operate at the edges of your role. Common prompts: "Tell me about a time you led a project end to end." "Tell me about a time you disagreed with your manager and how it resolved." "Tell me about a process you improved with measurable business impact."

The core signal they want is agency. Did you wait for requirements or did you identify the problem? Did you escalate or drive to resolution? Weak behavioral answers describe a team outcome without making your individual role legible. The interviewer cannot score what they cannot attribute to you specifically. "We did X" is not an answer. "I did X, which led to Y" is.

Where Strong Candidates Get Rejected

Jumping into SQL before clarifying the business question. "Find users with high retention" means nothing without knowing whether retention is measured on 1-day, 7-day, or 28-day windows, and whether it is per user or per session. Meta questions are deliberately underspecified. Asking one or two targeted questions before writing anything is not hesitation. It is the expected behavior.

Writing SQL that breaks on nulls and duplicates. Every production table has them. If your query uses an inner join without considering whether users exist in both tables, or aggregates a nullable column without a COALESCE, the interviewer will probe. "What happens if this column is null here?" is a follow-up candidates are rarely ready for.

Going silent while debugging. This kills SWE candidates and it kills data engineering candidates equally. If your SQL returns the wrong count, narrate while you trace it. Half-formed thoughts said out loud are better than perfect logic delivered in silence. The interviewer does not know you are thinking. They see someone who gave up.

Weak ownership stories. Many candidates spend 90 percent of their prep time on SQL and Python and wing the behavioral round. The ownership round is a distinct signal in the hiring packet. It can cause a downlevel even when the technical rounds are strong.

Over-engineering the data model. Nobody asked for a Lambda architecture.

Batman slapping Robin: "SQL databases do not scale" / "YouTube and Slack use SQL databases" / SYSTEM DESIGN INTERVIEW

Start with the simplest schema that answers the business questions. Scale when the interviewer introduces constraints, not before.

Meta Data Engineer Interview Prep: Six Weeks That Will Actually Work

Weeks 1 to 2: SQL fluency. Write five queries a day on real schemas. Cover window functions (all of them), funnel analysis, cohort retention, deduplication, and sessionization. DataLemur has good Meta-specific SQL practice. The goal is speed without losing correctness.

Weeks 3 to 4: Data modeling. Take a product feature you know (LinkedIn feed, Spotify listening history, Uber trips) and design a schema cold. Time yourself. Force the product sense section first. Then write three SQL queries against your schema and see if they fight it.

Week 5: Python and pipeline reasoning. Review sliding and tumbling windows, late event handling, and idempotent pipeline design. Know why you would choose Airflow over a cron job, and when streaming (Flink, Kafka) beats batch (Spark). You do not need to implement these from scratch, but you need to reason about trade-offs out loud without flinching.

Week 6: Behavioral prep and mock practice. Write out three or four ownership stories with concrete impact numbers. Practice saying them out loud. The ownership round is 30 minutes of unscripted conversation, and you will fumble the timing on stories the first few times you say them aloud. Better to fumble now.

If systems are rusty or you have been out of data engineering for more than a year, budget 10 to 12 weeks instead. The product sense plus schema plus SQL coupling is not something you can fake with polish.

For voice-based practice under interview conditions, SpaceComplexity runs realistic mock interviews with rubric-based feedback across all four scoring dimensions. That matters when you are prepping for a loop that evaluates communication as explicitly as Meta's does.

The Short Version

The phone screen has a hard pass bar: 3 of 5 SQL and 3 of 5 Python correct. Pacing is the test.
Every onsite technical round opens with 10 minutes of product sense. Do not skip it.
SQL and data modeling are coupled. Your schema shapes your query, and you are stuck with it.
Window functions, CTEs, and scale-aware thinking are assumed at every stage.
The Ownership round is scored independently and can cause a downlevel.
Clarify before you code. Narrate while you debug. Never go silent.
Prep timeline: 6 weeks if strong, 10 to 12 weeks if systems are rusty.