Meta Data Engineer Interview Loop
Meta's data engineer interview has three stages: a recruiter call, a one-hour technical screen that's half SQL and half Python, and an onsite with four interviews. Three of those four are "full-stack." You get one product scenario, and you design the data model, query it, and then process it in Python. People rarely fail here on hard problems. They fail on pace. And because the same scenario runs through all three technical rounds, a weak model hurts you three times.
What decides it
Being fast and clear on the basics. This isn't a software engineering interview with some SQL added on. Data handling and design count for more than algorithms. When someone gets a no, it's usually because they were slow on something they knew how to do.
Practice by round
The basics in SQL and Python, done quickly. The real screen sets a minimum number of correct answers in each half, so what it really tests is how fast you do things you already know.
SQL and Python at onsite level. In the real loop you'd write these against a model you drew ten minutes earlier, and the interviewer checks that your query fits your schema. So don't just run each one. Practice explaining it out loud.
| Question | Difficulty |
|---|---|
| Find whether the number of seniors works at Meta/Facebook is higher than its number of USA based employees | Medium |
| Meta/Facebook Matching Users Pairs | Medium |
| Liked' Posts | Medium |
| Spam Posts | Medium |
| Acceptance Rate By Date | Medium |
| Flattening Nested Lists | Medium |
| Max Value Key Sorting | Medium |
| Monotonic Array Checker | Medium |
| Consecutive Sum Finder | Medium |
| Array Into Bins | Medium |
| Balancing Frequency of Integers | Medium |
| Friend Count in User Array | Medium |
| Fans vs Opposition | Hard |
| Popularity Percentage | Hard |
| Balancing Special Text | Hard |
Start with the basics of design: how an engine runs a query, when a view is worth the storage, and UNION versus UNION ALL. Then come Meta’s design questions on its own features, like Friends You May Know and a restaurant recommendation, plus a project to build end to end. The real loop adds fact and dimension tables, grain and late data, and it builds on this reasoning.
| Case study | Difficulty |
|---|---|
| Subpopulations | Medium |
| Comparing Performance of Engines | Medium |
| Are We Friends? | Medium |
| GROUP or ORDER BY | Medium |
| Views and storage space | Medium |
| Union Versus Union All | Medium |
| Grain of a Reactions Fact Table | Medium |
| Friends You May Know | Hard |
| Restaurant Recommendation | Hard |
| Late-Arriving Events in a Daily Engagement Table | Hard |