Sharing my Pinterest DE loop experience from earlier this year. Mid-senior level. Virtual, 4 rounds.
Round 1: SQL. Live coding. I got a multi-table join scenario involving an events table and a users table, compute retention metrics. They asked me to write a 7-day and 30-day retention query. Pretty standard but they wanted efficiency discussion afterward. Asked about how I'd handle the query at Pinterest scale (billions of event rows). I talked about partitioning by date and pre-aggregated intermediate tables.
Round 2: Data modeling / system design. This one surprised me. It was half systems design and half data modeling. Prompt was roughly: design a data pipeline for Pinterest's Pin engagement events, from ingestion to analytics table. They want you to talk about schema design, partitioning strategy, streaming vs. batch tradeoffs (Pinterest uses Kafka at scale), and handling late-arriving data. Know your Kafka basics. Know what exactly-once semantics means and when it matters.
Round 3: Coding (Python/Spark). Not super heavy on Spark internals but they did ask me to write a PySpark transformation. The prompt was clean/transform a dataset with some schema evolution issues. Know how to handle nullable columns and schema mismatches.
Round 4: Behavioral. Very similar to other loops. Drive for results stories, cross-functional influence, handling a production incident.
Comp I was quoted: $200-240k TC for DE II in SF. Probably more negotiating room than they initially indicate.
Overall DE bar is real but not unreasonable. The main thing they care about is that you've thought about scale problems before, not just moved data from A to B.