i went through the Netflix data engineer loop earlier this year for a senior DE role. sharing because the internet has very little on what this loop actually looks like compared to their SWE process.
total rounds: phone screen + 4 technical rounds + culture contribution
the core technical areas:
SQL: two rounds had heavy SQL. and i mean heavy. Netflix uses Presto/Trino and Spark SQL. i got questions that required window functions, lateral joins, and handling NULL propagation across aggregations. one question was specifically about writing a query that identified users whose streaming quality degraded over a session - which required self-joins and window functions in the same query. i've been a DE for 7 years and it was hard.
pipeline design / architecture: i was asked to design a batch pipeline that ingests 10TB+ of daily event data, transforms it for analytics, and handles late-arriving records and schema evolution. the late-arriving records piece is where they went deep. how do you handle an event that shows up 48 hours late? does your pipeline reprocess? what's the cost? how do you make that decision? this is very real Netflix problem territory - they deal with data arriving from devices globally with wildly variable connectivity.
data modeling: one round was specifically about dimensional modeling and slowly changing dimensions. i got asked to design the data model for tracking a user's content preferences over time, including handling changes in their plan tier, geographic location, and device type. the interviewer wanted to know which SCD type i'd use for each dimension and why.
tools proficiency: Spark, Airflow, and dbt came up naturally in discussion. i was not asked to code in Spark but was asked about partitioning strategies, shuffle optimization, and how i'd tune a Spark job that was OOMing.
what i'd tell someone prepping: the SQL is harder than you expect. practice actual Presto-compatible window functions. the pipeline design needs to show operational maturity, not just architecture. data modeling seems old-fashioned but Netflix's analytics infra is deep and they care about it.