Finished the Nike data engineering loop last month. Beaverton-based role, data platforms team supporting digital commerce. Four rounds total, took about five weeks start to finish.
Phone screen was with a recruiter, mostly resume walk-through and a few behavioral questions. Standard stuff. The real thing started with the technical screen: 45 minutes, one SQL problem and one Python scripting question. The SQL was intermediate-hard. They gave me a schema with orders, customers, and product events tables and asked me to write a query returning rolling 7-day revenue by product category with a date filter. Window functions, CTEs, some aggregation. Nothing pathological but definitely not beginner level.
Python question was about parsing a semi-structured JSON log file and extracting error counts by error type. They cared about edge cases: malformed records, nested keys that might be missing, that kind of thing. I wrote clean code with explicit error handling and they seemed to like that.
Onsite was four back-to-back sessions over Zoom (this was remote):
Round 1: Pipeline design. Given a scenario where Nike's DTC sales data and wholesale partner data need to land in a unified analytics layer. Design the ingestion, transformation, and serving layers. I talked through batch vs streaming tradeoffs, brought up Airflow for orchestration, Snowflake as the warehouse. They asked about failure handling and exactly-once semantics, which I was glad I'd thought about.
Round 2: More SQL plus data modeling. Dimensional modeling question, fact vs dimension tables. They asked me to design a schema for tracking athlete endorsement activation data across campaigns. Specific to Nike, which was kind of interesting.
Round 3: Behavioral. Three STAR questions, fairly standard: conflict with a stakeholder, time you shipped something incomplete, how you handle data quality issues discovered in production.
Round 4: Bar raiser-ish. Senior engineer asked about distributed systems concepts, not super deep but they wanted to know I understood partitioning, replication lag, and why idempotency matters in pipelines.
Offer came in around 160-175k base for the Beaverton area, which felt a bit under market for the role scope. Negotiated up slightly. Nike isn't a software-first company so comps are a tick below pure tech.
Bottom line: prep SQL window functions, CTEs, and have a solid pipeline design story. The data modeling question surprised some of my peers who hadn't done it in a while.