Interviewed at HubSpot for a senior data engineering role in Q1 2026. Sharing because I couldn't find anything useful when I prepped, just outdated Glassdoor snippets.
Background on the role
This was on the data platform team, not a team-embedded DE. The difference matters for what they test: platform-side is more about pipeline reliability, orchestration, and helping downstream DS/analytics teams. Less about building one-off ingestion pipelines for a single product.
The loop (4 rounds)
Round 1: SQL
About 45 minutes, CoderPad. Three questions. One was straightforward joins, one involved window functions (lag/lead for calculating deltas between events), and one asked me to write a dbt-style incremental model pattern in SQL. That last one isn't pure SQL, it's SQL with business context. Know how incremental models work, what the merge logic looks like.
Round 2: Pipeline design
Big one. "Design a data pipeline that ingests CRM events from our product into the data warehouse in near real-time, with exactly-once semantics." 60 minutes. I talked through Kafka for event capture, Flink or Spark Structured Streaming for transformation, Snowflake as the destination. They were fine with my stack but asked a lot about the failure modes: what happens if your consumer falls behind, what happens if the Kafka topic doesn't have enough retention, how do you handle schema changes.
Key thing: they don't just want the happy path. Know your failure scenarios.
Round 3: Systems + tools
More conversational. They asked about my experience with Airflow (they use it), dbt, and how I think about data quality checks in pipelines. I talked through great_expectations briefly and they seemed familiar with it. Also asked about my approach to monitoring pipelines in production (alerting on row count anomalies, schema drift).
Round 4: Behavioral
Four STAR questions. The one I remember most: "Tell me about a time a pipeline you built broke in production and how you handled it." Very standard DE question. Have a good war story.
Overall
Fair process. They care about fundamentals: SQL, event streaming concepts, pipeline reliability. They're not testing obscure distributed systems theory. More "can you build and maintain reliable pipelines in our actual stack" than "can you recite the Kafka source code."