Did the Brex data engineer interview in late 2025, finally posting this. The loop was four rounds and the focus split was roughly 50% technical implementation, 30% data modeling/architecture, 20% behavioral.
SQL round: Two problems. One was straightforward aggregation and filtering on a transactions schema. The second was more interesting: they gave me a denormalized table and asked me to write a query that would break if certain data quality assumptions weren't met, then asked how I'd make it more defensive. That's a real-world data eng problem, not a LeetCode-adjacent thing. Liked that.
Pipeline design: No whiteboard in the traditional sense. They described a scenario: you receive raw card transaction events from a Kafka topic, need to land them in a data warehouse, and downstream analytics teams need both real-time dashboards and batch daily aggregates. Walk through your design. I talked about Flink for the real-time path, dbt for batch transformation, schema evolution with Avro, and monitoring with data quality checks at the ingestion layer. They pushed on the tradeoffs between the two paths (latency vs. cost vs. complexity).
Data modeling: They showed me a simplified version of their spend data and asked me to design a dimensional model for it. Star schema basics but also asked about slowly changing dimensions, which is where a lot of candidates get fuzzy.
Tooling they seemed to care about: Airflow or a similar orchestrator, dbt, some streaming background (Kafka at minimum), and familiarity with a cloud data warehouse (Snowflake/BigQuery/Redshift). Python for pipeline logic.
The DE role at Brex skews toward senior, so the bar felt appropriately high. Not unpassable, but you need to have actually built and operated pipelines, not just described them in theory.
Comp: I don't have a current number to share but Levels.fyi has some Brex DE data worth checking.