Just finished my GitLab DE loop last month. Sharing this because I couldn't find a single recent post when I was prepping, so here's the full picture.
The process was: recruiter screen, technical phone screen, then a 4-round virtual onsite. No take-home, which I actually appreciated.
Phone screen About 45 minutes with a senior DE on the team. Half was background, half was live SQL. Not tricky stuff, but not trivial either: window functions, CTEs, a question about deduplication logic. They asked how I'd handle late-arriving events in a streaming context. Know your basics cold.
Onsite (4 rounds, same day)
Round 1: SQL deep dive. Median salary query, ranking with ties, a join-vs-subquery efficiency question. They wanted me to talk through my reasoning, not just produce the answer.
Round 2: Data modeling. Design a schema for tracking CI/CD pipeline runs. Makes total sense given what GitLab actually does. They wanted to see how I handle slowly changing dimensions and what I'd denormalize for query performance.
Round 3: Systems design. Design a near-real-time data pipeline that ingests GitLab events (commits, MR opens, deployments) and feeds a dashboard. Kafka came up, so did dbt and Snowflake. They seemed fine with BigQuery too. The interviewer pushed me on partitioning strategy and failure recovery.
Round 4: Behavioral. STAR format, pretty standard. Biggest question was about a time I had to push back on a stakeholder request. They care about async communication skills, probably because the whole company is remote and async-first.
Comp for my offer: L3 equivalent, US remote, came in around $165k base with equity on top. Refreshes are reasonable from what I can tell.
Overall: the technical bar is real but not FAANG-tier hazing. They care that you understand data quality and can build reliable pipelines, not that you memorized graph algorithms. If you know dbt and have done serious Airflow or similar work, you'll be fine.
Happy to answer questions.