Just wrapped a Snap DE loop. Posting because most DE interview content out there is either ancient or generic. Snap's data infra is genuinely interesting and the interviews reflect that.
For context: I'm a senior DE, 7 years, mostly Spark + Kafka + dbt stacks. Interviewed for a senior role on the data platform team.
Phone screen (45 min): Mixed technical and behavioral. They walked through my resume, asked about the largest pipeline I'd managed (I described an event processing pipeline handling ~2TB/day), and then did two SQL questions live. One was a window function problem, the other was a recursive CTE for a hierarchy traversal. Neither was insane, but the recursive one caught me a bit off guard. Know your CTEs.
Onsite (4 rounds): SQL deep dive (60 min). More complex than the screen. Got a messy multi-join scenario with a tricky deduplication requirement. They specifically asked about performance, not just correctness. I talked through indexing strategy and why I'd avoid certain subquery patterns. Data modeling and architecture (60 min). This felt like a softer system design round. They gave me a scenario: Snap has a new ad product that generates events at high volume. Design the data pipeline from raw ingestion to reporting. I walked through Kafka ingestion, Spark streaming vs. batch tradeoffs, schema evolution, and how you'd build the downstream aggregation tables for the analytics team. Lots of good discussion on schema-on-read vs. schema-on-write. Coding (45 min). Python. Data processing problem, not LC algorithmic. More like: here's a dataset, clean it, aggregate it, return the result efficiently. Felt closer to real work than typical LC. Behavioral (45 min). Cross-functional stuff. How do you work with data scientists and analysts who have different quality bars than you? How do you handle a data incident that affects a downstream dashboard? Standard but worth prepping.
They use Spark internally and it came up naturally in round 2. You don't need to be a Spark expert, but knowing the fundamentals helps.
Overall: harder than some DE loops I've done, more interesting than most. The SQL rounds are real, not decorative.