Staff Product Manager

Responsibilities

• Own the vision, strategy, and roadmap for Render's datastores, anchored on Postgres. Scope includes provisioning and lifecycle management, high availability and disaster recovery, backups and point-in-time recovery, scaling and performance, connection management, and the extension ecosystem.
• Drive prioritization across Render's data investments, balancing foundational reliability work (replication, failover, storage performance) against near-term product and business needs, and negotiate those tradeoffs.
• Partner deeply with Engineering and other Product teams to shape the technical direction of Render's data layer, ensuring architectural decisions optimize for durability, performance, and operational simplicity.
• Develop a strong, opinionated point of view on how managed Postgres needs to evolve to serve AI-native builders—from vector workloads and retrieval pipelines to databases that are spun up, branched, and torn down programmatically by agents at scale.
• Set the standard for how developers experience data on Render, providing clear guidance, sensible defaults, and abstractions that make it easy for teams to run production databases with confidence—without needing a DBA on staff.

Requirements

• 7+ years in Product Management, with the majority spent on databases, data infrastructure, developer tools, or platform products.
• Familiarity with Postgres—its architecture, operational model, ecosystem, and the tradeoffs teams face running it in production.
• Deep developer empathy for both internal engineering teams and customers, and the technical depth to make sound architectural tradeoffs in collaboration with engineering.
• Track record of taking ambiguous, high-stakes reliability and infrastructure problems and turning them into clear strategy and shipped outcomes.
• Strong communication and storytelling skills.

Nice-to-Haves

• Experience as a database engineer, DBA, or infrastructure engineer.
• Hands-on experience with Postgres internals, replication tooling, or the extension ecosystem (e.g., pgvector, PostGIS).
• Experience building AI agents or the data infrastructure that powers them.