Did the Genentech DE loop a few months back. Most of what I read beforehand was generic or SWE-focused, so I'm writing the data engineer version.
Role: Senior Data Engineer, Research Informatics. South San Francisco, hybrid.
Process: Recruiter call -> HM call -> Technical screen (1 hour) -> Full loop (4 hours onsite or video for remote candidates)
Technical screen: This was the most immediately practical round. One SQL problem and one pipeline design discussion. The SQL was not trivial: multi-table join, window functions, handling missing values in a clinical dataset. They specifically asked about NULL handling in the context of patient data, which is a slightly different concern than regular analytics NULLs. Be thoughtful about what it means when a clinical measurement is missing vs. not measured vs. unknown.
Full loop: SQL and data modeling (60 min): Two problems. One schema design, one query optimization. They gave me a messy schema and asked how I'd redesign it for downstream BI use. Pipeline / architecture (45 min): They asked me to design an ELT pipeline for genomic sequencing data. Volume is large (think terabytes per batch), the transformation logic is complex, and the data is regulated. I talked about Airflow for orchestration, dbt for transformation, Snowflake or BigQuery for the warehouse, and spent a lot of time on data quality checks and logging. That last part felt important to them. Behavioral (45 min): Standard STAR questions but with a few domain twists, like "tell me about a time your pipeline produced incorrect results and how you caught and fixed it."
Tools they seemed to care about: Python, SQL, Airflow, dbt, at least familiarity with cloud storage (S3/GCS). They didn't seem to have a strong preference between Snowflake and BigQuery but asked me to compare them.
Comp (my offer, declined because of location): Base around 155k, 15% bonus target, RSUs around 45k/4yr. Reasonable but behind pure tech companies at the same level.