Engineering Manager, Data Platform

Sentry•San Francisco, CA

18h•$220,000 - $400,000

About The Position

Sentry is hiring an Engineering Manager to lead its Events Analytics Platform (EAP) team, which builds and operates the distributed data systems that power Sentry's products. The platform manages petabytes of telemetry data and provides high-performance analytical infrastructure for fast, complex queries across billions of records. It also provides the data foundation for Seer, Sentry's AI-powered debugging agent. The team is evolving storage and query infrastructure to handle growing data volumes, increasingly complex analytical workloads, and unified access to different types of telemetry. The Engineering Manager will lead engineers solving distributed-systems challenges, guide technical and architectural decisions, and balance infrastructure improvements with production reliability and performance. The role works closely with product engineering teams to deliver capabilities that affect the developer experience. This position combines people leadership with close involvement in technical discussions, architecture, and complex problem-solving. The manager will set priorities, drive projects from design through production, develop the team, and ensure the platform supports Sentry's products and AI-powered experiences.

Requirements

Significant professional software engineering experience, including experience managing and developing engineers, leading technical projects, and driving engineering execution.
Strong technical background in distributed systems, data infrastructure, analytical databases, or large-scale backend systems.
Hands-on experience building, scaling, or operating complex data-intensive systems, with depth in analytical database infrastructure or complex distributed data platforms.
Experience operating, scaling, optimizing, or maintaining production OLAP systems such as ClickHouse, Apache Pinot, Apache Druid, or similar technologies; or designing and building sophisticated systems using analytical databases or distributed storage technologies as foundational components, with meaningful ownership of architecture, performance, and reliability.
Experience making architectural decisions and technical tradeoffs involving scalability, performance, reliability, or operational complexity.
Demonstrated ownership of production systems, including troubleshooting, incident response, performance optimization, and operational improvements.
Experience leading engineering projects from technical design through implementation, deployment, and ongoing operations.
Strong communication and collaboration skills, including working with cross-functional stakeholders and translating technical requirements into actionable engineering priorities.

Nice To Haves

Experience with ClickHouse, Pinot, Druid, or other column-oriented analytical databases.
Experience with query optimization, analytical storage architecture, or high-throughput data processing.
Experience building infrastructure for observability, telemetry, developer tools, or event-driven applications.
Experience working closely with product engineering teams to develop infrastructure supporting customer-facing features.
Proficiency with Python, Rust, or similar backend programming languages.

Responsibilities

Lead and develop a team of engineers working on large-scale distributed data systems, fostering technical excellence, ownership, collaboration, and professional growth.
Drive technical execution and architectural decisions across EAP's storage and query infrastructure, balancing scalability, reliability, performance, and long-term maintainability.
Build and evolve analytical data systems that support complex queries, high-throughput workloads, and efficient access to petabytes of event data.
Guide architectural evolution, including query optimization, storage and compute separation, and unification of different event types into a common analytical platform.
Own the platform's operational health by improving reliability, troubleshooting production issues, managing incidents, and addressing scaling challenges.
Partner with product engineering teams to understand technical requirements, make informed tradeoffs, and deliver platform capabilities for new product experiences.
Ensure the platform efficiently serves analytical workloads and production context needed by Seer and other AI-driven experiences.
Set clear priorities and drive projects from design through production while managing competing technical and product needs.