Went through the Scale AI data engineer interview loop recently. Posting this because the info out there is sparse and mostly about ML/SWE roles.
For context: I came in as a senior DE candidate, 7 years experience, mostly Spark, Airflow, and Redshift/BigQuery in fintech. The loop had four rounds, all technical.
SQL round. This was heavier than I expected. They didn't just want selects and group bys. The scenario was modeled around time-series annotation data: given a table of task completions with timestamps and worker IDs, write queries to identify throughput drops by hour, and flag workers whose error rate spikes in the last week relative to their historical baseline. The historical baseline part required a self-join with a subquery or a CTE. We went with CTE because it was readable. Then they asked how I'd optimize this query if the table had 500M rows. Partitioning by date, clustering by worker_id, materialized views for the baseline calculation. Standard answers, but they pushed on the tradeoffs.
Pipeline design round. This was the most realistic round. The question: design a pipeline that ingests annotation events from multiple task types, standardizes them into a canonical schema, and routes them to downstream quality checks and billing systems. I talked through Kafka for the event bus, a schema registry to handle schema evolution across task types, Spark Streaming or Flink for the transformation layer, and a write path that lands into both a Delta Lake table (for analytics) and a Postgres sink (for billing). They asked specifically about late-arriving events and how I'd handle deduplication. I talked through watermarks and idempotent writes. Good discussion.
Coding round. A Python problem. Write a function that takes a list of annotation events and returns aggregated quality metrics. Nothing exotic. Mostly about clean code and edge case handling. Watch out for None values in the input. They cared that I handled those gracefully.
Behavioral round. One hiring manager behavioral session. Standard questions but probing: tell me about a pipeline that broke in production, what you'd do differently. They were listening for ownership and postmortem thinking, not just 'we fixed it.'
Loop took about 3 weeks from first screen to offer. Base offer came in around $185k for the senior DE level in SF. I pushed back once, got another $10k on base, RSU grant didn't move. Worth trying.