Beamery's unique jobs, skills and tasks data platform helps organizations navigate challenges and make more informed decisions across Talent Lifecycle Management.
Solutions power recruitment, mobility, upskilling, diversity, work architecture and workforce planning.
Deepening native integrations with SAP, Workday, Microsoft, and LinkedIn.
Embedding agentic AI to help customers plan smarter.
Advancing use of proprietary LLMs and knowledge graph technology.
Responsibilities
Own the Data Foundry vision, strategy and roadmap, framing each item as a bet with a clear driver, aligned to overall strategic objectives.
Ensure alignment of Data Foundry strategy to Beamery's AI strategy, reflecting the shift from providing AI assistants to providing AI access through MCP and API channels.
Decide what Data Foundry provides as internal infrastructure and what is packaged and sold as a customer-facing product.
Make and defend build versus buy decisions across integrations, analytics, AI and taxonomy capabilities.
Discover and validate what external customers want from data-as-a-service, and partner with Sales and Customer Success to drive adoption with clear GTM and enablement plans.
Build the case for Data Foundry as a new commercial channel, defining and proving the standalone commercial model.
Lead your team to deliver solutions our customers can't imagine working without.
Agree and maintain the data contracts with the CRM and Workforce Suite product managers.
Prioritise across data science, data engineering and integrations engineering.
Communicate transparently and create alignment between peers and leadership.
Manage customer and sales expectations through well managed structured cadences.
Own how workforce data is governed, secured and access-controlled as it is exposed through APIs and MCP.
Ensure the outputs of Data Foundry's models are measurable and explainable.
Requirements
A product manager experienced in complex technical, data or AI products, ideally in enterprise B2B SaaS.
Strong grasp of data platforms, APIs and integrations, comfortable making architecture-adjacent decisions with engineering and data science leads.
Experience handling sensitive or regulated data, with a clear view of governance, security and privacy.
An analytical thinker who uses data to find opportunities, prioritise and measure success.
A confident prioritiser who can balance competing tribe roadmaps and internal customers without losing direction.
Commercially fluent, able to connect product decisions to revenue, cost and customer ROI.