GitLab
Senior
Engineering Manager leading GitLab's Data Insights Platform and classic search architecture
Lead a distributed-systems platform team at GitLab building the ingestion-to-query pipeline (Siphon CDC, NATS/JetStream, ClickHouse, Query API) that powers self-service reporting and search across GitLab.com, Dedicated, Self-Managed, and Cells deployments. A hybrid role: roughly half people management, half hands-on architecture ownership for high-throughput event systems.
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What this interview tests
- Distributed data pipeline architecture (ingestion, buffering, enrichment, storage, query serving)
- Engineering management: hiring, coaching, roadmap ownership, delivery accountability
- Multi-deployment-model tradeoffs (SaaS vs Dedicated vs Self-Managed vs Cells)
- Service boundaries and cross-team API ownership (platform vs feature teams)
- Operational maturity: observability, capacity planning, incident readiness
- Using AI to drive team productivity
Common question themes
Tell me about a time you balanced hands-on architecture work with people management
How would you design backpressure handling into a CDC-based ingestion pipeline at scale?
How do you decide where to draw ownership boundaries between a platform team and consuming product teams?
Walk me through how you'd support the same data feature across GitLab.com, Dedicated, and self-managed deployments
How have you used AI to change how your team ships or operates?
Describe a production reliability incident you led the response to
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