Sr. Product Manager, Core Data Platform

Please Note: This is a Utah-based hybrid position which will require some regular in-office days each week. Additionally, employment with BambooHR is contingent on passing both a background and credit check. AI at BambooHR At BambooHR, we’re all about setting people free to do great work, and we believe AI is a powerful partner in that mission. We’re leaning into intelligent tools to streamline our workflows, giving us more time for high-impact innovation. We look for curious, forward-thinking people who are ready to explore how AI can elevate their work and help us reimagine the future of HR. Essential Job Duties We're looking for a Senior Product Manager to drive the strategy and execution of BambooHR's core data platform: the foundational layer that defines how every system, integration, and AI experience understands our data. You'll play a key role in shaping how BambooHR represents its data internally and how that foundation powers the AI-driven experiences our customers depend on. This is not a traditional product role. You won't own a customer-facing UI. You own the layer underneath: the data definitions, contracts, and adoption process that make every product team faster, every integration simpler, and every AI interaction more accurate. Your customers are primarily internal, and when the platform is right, every team benefits. As the Senior Product Manager for this space, you'll coordinate across product and engineering teams to define and evolve the canonical data model, partner with architects to make hard technical tradeoffs, and connect foundational platform work to real customer outcomes. You will: Drive alignment on data definitions across product and engineering teams, resolving conflicts and locking decisions that the rest of the organization can build on. Coordinate a multi-team, multi-quarter effort without direct authority over the teams doing the work. Partner closely with principal architects and engineering leads to make principled data modeling tradeoffs and communicate the implications broadly. Ensure the data platform is easy to build against: clean APIs, well-documented contracts, and a clear upgrade path as the model evolves. Build th e internal case for platform investment by connecting foundational decisions to real outcomes for integrations, AI features, and customer-facing products. Own the data catalog and validation process, ensuring coherence as new domains are onboarded and new entities are proposed. Measure adoption and coverage. Know at any point how complete the model is, where gaps exist, and what is blocking progress. Work closely with AI platform teams to ensure the data model serves as a reliable foundation for AI-driven experiences.