Updated by Meta candidates
Senior Data Engineer Interview Experience
The interview is definitely hard, but achievable with preparation because the timing is very less. In the screening, you get five Python and five SQL in one hour, and you need to pass at least three SQL and three Python.
Interview process
I interviewed for Meta IC5 Senior Data Engineer, and the process felt hard but achievable if you prepare the right way. The first big hurdle was a live 1-hour CoderPad screening with 3 SQL and 3 Python questions, and the hardest part by far was the time pressure. After that, the onsite was four rounds and felt much more analytical than other DE or backend loops I've done, especially around SQL, data modeling, and tradeoff thinking. The round that stood out most was designing an Uber-style data model and then defending scaling choices like bucketing and hash-based partitioning once the table size went past 1 TB. Overall, it felt very SQL-heavy and very focused on whether I could show real technical depth, not just solve toy problems.
- Recruiter screen
- Technical interview
- Final round
Interview tips
I'd do a lot of Python and SQL practice, but do it with a clock because the time constraint is a huge part of this process. I'd also start with a brute-force solution first so I have something working, then improve it if I still have time. For the onsite, don't stop at the schema or code. Be ready to explain tradeoffs, partitioning choices, and why your design will still work at scale.
Company culture
What I saw is that Meta's DE loop is more analytical and a lot more SQL-focused than other data engineering loops I've done. Even when the prompt starts as data modeling, they quickly move into improvements and scale tradeoffs like partitioning strategy, bucketing, and lookup efficiency, so it feels like they're checking technical depth more than memorized answers. The process also felt pretty structured: tight screening first, then a standardized multi-round onsite. The recruiter was transparent on compensation early and framed it as total comp.
Questions asked
Overview
My onsite had four rounds, and the overall pattern was data modeling, product analytics, and some SQL/Python mixed in. The round I remember most felt very DE-specific because they cared about schema design, scaling tradeoffs, and then a streaming-style Python problem.
Question types asked
Specific questions asked
What tables, primary keys, foreign keys, and one-to-many or many-to-one relationships would you define?
How would you optimize the transactional table when the volume grows beyond 1 TB?
What partitioning technique would you use, and how would you think about round robin versus hash-based partitioning?
I laid out a relational schema with the core entities like users, drivers, rides, payments, and locations, and defined the PK/FK relationships. Then they pushed on scaling and asked how I'd optimize the transactional table past 1 TB. I said I'd use bucketing based on a date timestamp, and when they got into partitioning tradeoffs I said hash-based partitioning made sense because it's easier for lookup at volume. That part felt like a pure technical depth check.
Given a stream of Uber ride requests, compute the number of ride requests in every 15-minute window.
Assume a tumbling window.
I treated it as a streaming aggregation problem and computed ride-request counts in 15-minute tumbling windows. The question itself was pretty direct, but it matched the rest of the loop in that they wanted something practical and data-engineering oriented, not just generic Python syntax.
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