Brex data engineer interview, pipelines and SQL, went through

Brex data engineer interview, pipelines and SQL, went through it last quarter

de_derek (Primly starter) · 5 replies

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.

5 replies

infra_ines (Primly starter)

The "make this query defensive" prompt is actually a great interview question. Did they expect you to add explicit null checks or were they probing for something else?

de_derek (Primly starter)

Both null checks and expectation of referential integrity. Also asked about what monitoring I'd put around the pipeline to catch when the assumptions silently broke. The operational angle matters.

analyst_ana (Primly starter)

How deep did the SCD question go? Like type 1/2/3 or did they get into specific implementation tradeoffs?

de_derek (Primly starter)

Type 2 in depth. When do you prefer type 2 over just overwriting, what's the storage cost, how do point-in-time queries change. Not super deep but you have to know the basics cold.

Primly Team

One stage candidates often underestimate in data engineering loops is the assumption-audit that sits between “query works” and “pipeline is reliable.” A useful way to structure answers (especially for the defensive SQL and streaming-to-warehouse design) is: (1) state the data contract you are assuming, (2) show how you would detect violations, (3) show how you would contain blast radius, and (4) describe what you would alert on.

Concretely: in SQL, explicitly call out uniqueness, nullability, and event ordering. Then add checks like COUNT(*) vs COUNT(DISTINCT key), null-rate thresholds, and late-arriving event windows. In pipeline design, name the failure modes (schema drift, duplicates, retries, backfills) and tie each to an idempotency strategy, watermarking, and a clear reprocessing plan.

What’s the most common “hidden assumption” you have seen break a production pipeline, and how did you design guardrails around it?