Meta Data Engineer Interview Questions: what the tech screen really tests
Search for Meta data engineer interview questions and you get the same thing every time: long dumps of SQL prompts and Python snippets, stripped of the one detail that actually decides the result. The Meta data engineer screen is not hard in the way a tagged problem list suggests. It is timed, it is eliminatory, and the people who fail it mostly fail on pace — not on whether they know a window function, but on whether they can write it cleanly while the clock runs and the interviewer watches.
This guide maps how the loop actually runs in 2026: the recruiter screen that quietly sets the bar, the hour-long technical screen split between SQL and Python, and the onsite rounds that move from a product question to a data model to a query — all on one scenario, all building on what you just said. It covers the data engineering interview questions each stage tests, the follow-ups that catch strong candidates, and how to prepare for the part no question dump prepares you for: doing it out loud, at speed.
How the Meta data engineer loop runs (2026)
Meta's process is a loop with a sharp gate in the middle. A typical path runs recruiter screen, then the technical screen, then an onsite — and the technical screen is eliminatory, so most of your prep should aim at clearing it.
- Recruiter screen (~30 minutes). Background, past projects, and your comfort with SQL and Python. It feels like logistics, but it does one important thing: it locks in the coding language you will use, and the level the recruiter sets is what you are graded against for the rest of the loop. Pick the language you are fastest in and say so clearly.
- Technical screen (~60 minutes). Roughly 25 minutes of SQL, then 25 minutes of Python, in a plain shared editor with no autocomplete. Expect about three SQL and three Python questions (some report five and five), with a minimum number correct in each half to advance. None of them is individually brutal; the difficulty is that there is no time to stall. Pause on a join and you are already behind.
- Onsite (four rounds). Usually three "full-stack" data-engineering rounds and one ownership or behavioural round. The full-stack rounds are the real test, and they are covered in their own section below.
On the screen the interviewer rarely lets a working query sit. "That returns the right numbers — now rewrite it without the subquery, and tell me which one the planner prefers." The screen measures whether you can produce clean SQL and reason about it at speed, not whether you eventually arrive.
The SQL the tech screen tests
Meta's data engineers write SQL against enormous tables, so the screen checks that your fundamentals are fast and correct, not that you know exotic tricks. The reported topics are consistent: joins, aggregations, window functions, self-joins, set operators, subqueries, and multi-table queries that answer a business question. The standing advice from people who have passed is to go simple — reach for a clean window function over a tangle of nested subqueries, because the clean version is faster to write, easier to explain, and usually faster to run.
The single most-tested shape is a ranking or running calculation inside a group, which is exactly what a window function is for:
-- rank each user's events by date, then keep the first — the Meta screen's favourite shape
SELECT user_id, event_date
FROM (
SELECT user_id, event_date,
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_date) AS rn
FROM events
) t
WHERE rn = 1;
If you can write that without thinking, explain why PARTITION BY resets the count per user, and adapt it when the interviewer asks for "the second event" or "events per user per week", you are ready for most of the SQL half.
The Python the tech screen tests
The Python half surprises people who prepared for it like a coding interview. It is not about graph algorithms or dynamic programming — it is data manipulation with the standard library. Expect dictionaries, lists, sets, string parsing, and small functions that transform one shape of data into another. A common prompt is "count the occurrences of each character and rewrite the string in that format", which is a dictionary problem, not an algorithm problem.
# Python screen leans on dicts/lists, not algorithms: count, then rebuild
from collections import Counter
def encode(s):
counts = Counter(s) # {'a': 2, 'b': 1, ...}
return "".join(f"{ch}{n}" for ch, n in counts.items())
Write it, say what Counter is doing, and mention the edge cases (empty string, case sensitivity) before you are asked. The Python half rewards clean, idiomatic code delivered quickly far more than cleverness.
The onsite: product sense → model → query
Candidates walk into the Meta DE onsite expecting one SQL round, one modelling round and so on. That is not how it works. It is closer to the same interview three times on a different product scenario each time: about ten minutes of product sense, then the KPIs that matter, then a data model (the schema) to support them, then the SQL — and sometimes Python — to answer the question, each step building on what you just said. One of your scenarios often is not even a Meta product. The modelling step draws the most follow-ups, usually about why you chose a particular dimension or fact table.
The most expensive mistake here has nothing to do with syntax. As one Meta data engineer put it to a candidate afterwards: you designed the tables before asking what the product needed, so your schema was clean but answered the wrong question. The onsite is a reasoning chain you defend out loud — clarify the product goal, pick the metrics, then model, then query — and jumping to the schema is how strong engineers quietly fail. Some loops also include an ETL design or debugging discussion inside these rounds.
Each round is read on the four dimensions a scored data round grades: problem solving (did you pick the right metrics and model), communication (did you narrate the product goal before the schema), technical execution (clean SQL and Python that run), and time management (did you move through the chain without stalling). A question dump trains none of those.
Data engineering
Meta · Tech screen (SQL + Python) · 60 min
Run a timed Meta DE tech screen out loud, SQL then Python
How to prepare for the Meta DE screen
The prep that works mirrors the test: fast, out loud, and timed. The prep that fails is reading solved SQL in silence.
- Practise back-to-back under a clock. Do SQL and Python in one 60-minute block, not in separate calm sessions. The whole skill is sustaining pace across both halves.
- Narrate as you type. Say the join you are about to write and why; say what the window function partitions on. The screen is graded on clarity under time, so silence costs you even when the query is right.
- Go simple on SQL. Default to a window function over nested subqueries; it is faster to write and to explain.
- Ask before you model. In the onsite, spend the first minutes on the product goal and KPIs. A schema that answers the wrong question is the classic Meta DE failure.
- Know the metrics. Skim what Meta actually tracks (engagement, watch time, impressions); product-sense prompts lean on them.
If you need raw volume of SQL and Python data-engineering reps to build the speed first, PipeCode is a sister platform built for exactly that kind of timed DE practice; use it to get fast, then come back here to rehearse the screen and the onsite chain out loud.
Data engineering
Meta · Tech screen set · 60 min
Rehearse the product-sense → model → query chain with follow-ups
Frequently asked questions
What does the Meta data engineer interview process look like?
It is typically a recruiter screen, a timed technical screen, and an onsite. The recruiter screen covers your background and locks in your coding language. The technical screen is about an hour, split between SQL and Python, and it is eliminatory — you need a minimum correct in each half to move on. The onsite is usually four rounds: three full-stack data-engineering rounds that mix product sense, data modelling, SQL and Python, plus an ownership or behavioural round. The rounds here are modelled on how that loop runs, not sourced from any leaked question set.
How many SQL and Python questions are in the Meta tech screen?
Most candidates report about three SQL and three Python questions in 60 minutes, split roughly 25 minutes each, though some see five and five. The exact count matters less than the pace: the questions are not individually hard, but there is almost no time to think, so stalling on one costs you the next. People who are rejected after the screen most often say pace, not difficulty, was the problem.
What SQL topics does Meta test data engineers on?
Joins, aggregations, window functions, self-joins, set operators, subqueries, and multi-table queries that answer a business question. Window functions — ranking, running totals, first or last event per group — come up constantly, so they should be automatic. The advice from people who passed is to keep it simple: a clean window function usually beats a stack of nested subqueries on both readability and speed.
What kind of Python does the Meta DE screen ask for?
Data-manipulation Python with the standard library, not algorithms. Expect dictionaries, lists, sets, string parsing and small transform functions — for example, counting character frequencies and rebuilding a string, or reshaping one data structure into another. Clean, idiomatic code written quickly scores better than anything clever, and you should state the edge cases (empty input, case sensitivity) as you go.
What happens in the Meta DE onsite rounds?
The onsite is usually three full-stack rounds plus an ownership round. Each full-stack round runs the same chain on a different product scenario: product sense, then KPIs, then a data model, then SQL and sometimes Python — each step building on the last. The modelling step draws the most follow-ups, often about why you chose certain dimension or fact tables. The common failure is jumping to a schema before clarifying what the product actually needs.
How do I practise the Meta DE screen out loud?
Reading solved queries is not the same as writing them at speed while someone watches. On Zynter, the Data engineering track runs Meta tech-screen sets — three Python and three SQL questions in 60 minutes against a real schema — with a voice interviewer that follows up on your approach, then a debrief on problem solving, communication, technical execution and time management. You get a genuine data-engineer mock interview on demand, and the score is a practice signal, not a hiring decision.
Practise it on Zynter
You can read a hundred Meta SQL questions. The screen is won on the six you write cleanly, at speed, out loud.
Pick a Meta tech-screen set, write SQL and Python against a real schema, and talk through each answer with a voice interviewer that follows up on your joins, your window functions and your data model — then read the debrief. Zynter.ai is on-demand mock interviews modelled on real company loops, with 150+ rounds across 20 companies (Sep 2026) — about $8–$12 for a full 60-minute mock, and two free credits to start.
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