GitHub is changing the way the world builds and secures software, and we want you to help build GitHub!
Over 100 million developers use GitHub to build, ship, and maintain their software. Underneath every repository, every pull request, and every AI-powered workflow is a data infrastructure that has to handle unprecedented scale, stay resilient, and evolve fast enough to power what GitHub can offer next. As AI-driven development accelerates, the demands on that infrastructure keep growing, and the data layer is where the most consequential architectural decisions get made.
We're looking for a Staff Product Manager to join the Data Services team within Platform. Data Services owns the foundational data platforms GitHub runs on: relational and non-relational storage, caching, event streaming, search, and object storage. These systems underpin everything from core Git operations to Copilot, and how they evolve will power what GitHub delivers for the next generation of developers and AI workloads.
This is a role where you shape the future of GitHub's data platform. You'll partner closely with engineering to define strategy across a broad and complex portfolio, drive alignment on the decisions that determine how GitHub scales into the AI era, and own the product direction for data infrastructure that hundreds of millions of developers ultimately depend on.
Define and drive the product vision and strategy for GitHub's data infrastructure, spanning MySQL, Kafka, Elasticsearch, Redis, object storage, and next-generation data primitives.
Define the strategy for evolving GitHub's data platform portfolio, determining where to scale and optimize existing systems and where adopting modern cloud-native services best serves GitHub's reliability and scale requirements.
Lead the product definition for GitHub's multi-region data architecture, driving alignment on per-service consistency models, replication strategies, and data-layer failover design and RTO/RPO tradeoffs.
Drive the product strategy to scale GitHub's data layer by 10x to 100x, ensuring foundational platform capabilities keep pace with AI-accelerated development and next-generation product features.
Define the data model interaction layer and best practices for internal teams building customer-facing applications and features, so product teams can build on data primitives without needing to be infrastructure experts.
Define metrics that measure infrastructure health, migration progress, and reliability outcomes, and use them to drive investment decisions and hold engineering accountable on results.
Required Qualifications:
Preferred Qualifications :
Master's Degree in a relevant field.
Strong technical foundation with the ability to engage credibly with senior engineers on architecture decisions involving data storage, replication, consistency, and distributed systems tradeoffs.
Experience owning cloud migration or modernization strategy for data infrastructure, including evaluating tradeoffs between scaling self-hosted systems and adopting managed services.
Experience defining product requirements for multi-region data architectures, including per-service consistency models, replication strategies, and data-layer failover design.
Experience defining platform contracts or paved paths for data primitives, enabling application teams to adopt standard caching, search, or event streaming patterns without bespoke infrastructure work.
Experience building product strategy for AI-native data capabilities such as vector search, semantic retrieval, or high-throughput event pipelines that serve both human and machine workloads at scale.
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