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Engineering Manager, Data Platform

Sentry - San Francisco, CA, United States - In-office - posted 2026-07-31

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Salary: USD 220,000 - 300,000 / annual

Sentry is a developer-first observability platform trusted by 200,000+ organizations and 4 million developers worldwide. The company helps developers fix errors and performance issues before users notice, enabling teams to spend less time firefighting and more time building. You will lead the Events Analytics Platform (EAP) team, which is at the heart of Sentry's mission. EAP powers how all of Sentry's event data—errors, transactions, spans, profiles, replays, and metrics—is stored, queried, and analyzed. The team also powers Sentry's latest AI push, Seer, and is a cornerstone of the company's long-term strategy to become a context assembly and telemetry platform. As Engineering Manager, you will: - Grow and develop a team of engineers with high expectations for ownership and impact - Set technical and strategic direction, balancing short-term stability with long-term architectural evolution - Drive development of core EAP features including complex analytical queries, dynamic routing logic, storage/compute separation, and modern analytical storage patterns - Ensure EAP supports workload demands of AI agents and MCP servers - Guide unification of all event data into EAP and enable fast, complex cross-event querying at scale - Partner with Product Engineering, Streaming, Production Engineering, Security, and Compliance teams - Lead the team through incidents, postmortems, and scaling challenges while fostering operational excellence - Build a healthy, collaborative team culture rooted in growth, accountability, and inclusion You should have 10+ years of software engineering experience including 4+ years of people management with career development and performance management responsibilities. Strong technical background in data platforms, storage systems, or analytical backends (ClickHouse, Pinot, Druid) is essential. Experience with distributed systems, query optimization, and scaling data-intensive workloads is required. Proficiency with Python, Rust, or similar languages and familiarity with observability, developer tools, or event-driven systems are pluses.

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