Meta Senior Data Engineer Interview Experience
Meta · Senior · Data Engineer
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.
Interview rounds · 3
- 1
Recruiter screen
The recruiter mostly set up the process and shared the total comp range pretty early, around 350K to 400K TC, so I had a sense of level and expectations before the technical rounds.
- 2
Technical round
SQLCodingTechnicalThe screening was a live 1-hour CoderPad with a quick intro and then 3 SQL plus 3 Python questions. The hard part was not that the problems were impossible, it was the time pressure and the fact that you needed to clear at least 3 SQL and 3 Python.
Q1. Given a library-style schema with books, users, and check-in/check-out data, write a query to find for each user the number of books they checked out and did not return before the due date, and return the username and count.
How they answeredI treated it like a multi-table join across the books, users, and checkout tables, then counted overdue unreturned books per user and returned the username with the count. The main thing was getting the joins and filtering around the expiry date right under time pressure.
Q2. Given users, books, and checkout data, find the number of users who checked out books in the geography category grouped by age.
How they answeredI grouped the result by user age and counted users who had checked out geography-category books. This one was more straightforward, but in that round even the simpler SQLs mattered because the clock was so tight.
Q3. Find the number of people who checked out a book on the same day that another person returned that same book.
How they answeredI handled it as a same-book, same-day condition between a return event and another user's checkout event, then counted the people matching that pattern. It was one of those questions where you had to be very careful with the event logic and not overthink it.
Q4. Given a list of strings, find the number of occurrences of the second letter of the first word, and return the character and its count.
How they answeredI used a hashmap or array approach. The input was a list of strings, and I returned the character and the number of occurrences. I also accounted for the edge case around adjacent characters and cases where the second character logic could fail, so I would skip or handle those cleanly instead of forcing it.
Follow-up questions- What edge cases would you handle?
Q5. Write a function to read and process a CSV data file, and handle the case where the file is not found.
How they answeredThis one was really about structured error handling. I used try/except while reading the CSV and processing it, and if the file was missing I returned or printed 'file not found.' They seemed to care that I handled it in a clean Pythonic way, not just that I could read the file.
Follow-up questions- How do you handle exceptions in Python when processing data files?
Q6. Given employee, department, and salary data stored in a dictionary, find the second maximum salary for each department.
How they answeredI grouped salaries by department and then found the second highest salary for each one. It was a typical data manipulation problem, but again the challenge was doing it fast and correctly because there were a lot of test cases.
- 3
Final / onsite round
Data ModelingData Pipeline DesignSQLCodingAnalyticalMy 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.
Q1. Design a data model for an Uber ride app.
How they answeredI 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.
Follow-up questions- 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?
Q2. Given a stream of Uber ride requests, compute the number of ride requests in every 15-minute window.
How they answeredI 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.
Follow-up questions- Assume a tumbling window.
Tips from the candidate
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.