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Role Summary

We are building a modern, AI-enabled HR organization where data and intelligence are embedded into every product, workflow, and decision.

The Principal Product Manager, Workforce Intelligence is responsible for defining the vision and driving the strategy for how HR data is structured, governed, analyzed, and delivered across the enterprise. This is ahigh-impact, high-influence positionfor someone who leads through expertise, vision, and the ability to align teams around a shared direction.

This role owns the product strategy for the full HR intelligence stack-from the foundational data architecture that makes reliable analytics possible, to the reporting systems that drive operational decisions, to the AI-powered tools that put predictive insight directly in the hands of managers, HR partners, and leaders. The Principal PM will serve as the connective tissue betweenHR functional teams(Talent Acquisition, Talent Management, Total Rewards, Employee Experience, and People Operations, etc.) and thetechnical teams-Data Engineering, HR Technology, and People Analytics-who build and maintain these capabilities.

Success in this role is measured by the clarity of the vision set, the quality of the products built, and the degree to which data and intelligence change how HR operates across the company.

How This Role Works

This roleoperates at the intersection of business strategy and technical execution-translating HR priorities into product direction and influencing what gets built, in what order, and to what standard.

The Principal PM will:

• Set theproduct vision and roadmapfor HR Workforce Intelligence
• Influence and alignData Engineering, People Analytics, and HR Technology teams around that vision
• Partner with HR functional leaders to understand their data needs and translate them into scalable product solutions
• Work hand-in-hand with theDirector of People Analyticsto ensure strategy and execution are tightly connected
• Drive decisions throughclarity, credibility, and structured thinking

Key Responsibilities

Enterprise HR Data Foundation

Define the product strategy for the underlying data infrastructure that makes all HR analytics trustworthy and scalable. This includes:

• Establishing the vision for theenterprise HR data architecture-how data flows from source systems, how it is modeled, and how it is made available for consumption across products and teams
• Driving alignment onsystem integrationsbetween HR platforms, ensuring that data moving between systems is consistent, complete, and reliable
• Owning theanalytics semantic layerstrategy-the standardized definitions, business logic, and governed metrics that ensure every team is working from a single, trusted source of truth
• Leading the strategy and evaluation process for thereporting platform infrastructure for how HR data is accessed and delivered at scale

Establish Trusted, Scalable Reporting

Ensure the organization has well-governed, reliable reporting that supports both operational rhythms and strategic decision-making. This includes:

• Definingfoundational HR metrics-the core set of workforce KPIs that anchor all reporting and are consistently understood across HR and the business
• Setting the product direction for theoperational reporting cadence, including the data products and pipelines that support Weekly Business Reviews (WBRs), Quarterly Business Reviews (QBRs), and executive-level reporting
• Driving the strategy for products like performance reporting suitethat gives leaders, HR partners, and managers consistent, trusted visibility into workforce outcomes and glidepath reportingto track progress against workforce targets and strategic workforce plans over time

Champion Employee Data Quality and Governance

Define the product standards that ensure employee data is accurate, governed, and trustworthy as the foundation for all downstream analytics. This includes:

• Setting the strategy foremployee data management-how employee records are created, maintained, and trusted as the authoritative source across HR products
• Influencing theHCM platform and HR Technology teamson data ownership, governance standards, and how employee data administration processes are designed and maintained
• Establishing product principles and standards fordata quality, access controls, and sensitive data protectionacross all HR data products-balancing broad access to insight with rigorous protection of personal and sensitive information

Scale Self-Service and Insight Delivery

Build the frameworks and products that enable HR partners, managers, and business leaders to access data and insights without creating a centralized bottleneck. This includes:

• Owning theself-service reporting platform strategy-defining the right tools, interfaces, and access tiers that allow HR Business Partners and business leaders to answer their own questions confidently and independently
• Driving the development of anHRBP Toolkit-a curated suite of data products, dashboards, and intelligence resources that enable HR Business Partners to show up to business conversations with consistent, high-quality insight
• Defining and advocating for adelivery modelthat scales analytical capacity across the HR organization, ensuring that insight reaches the people who need it without requiring every request to flow through a central team
• Establishing the principle that insights should be deliveredin workflow and in context-embedded in the tools people already use, not in disconnected reports or portals

Define and Drive the AI and Predictive Analytics Roadmap

Lead the vision for how AI and predictive modeling transform the way HR operates and how people decisions are made across the company. This includes:

• Identifying, prioritizing, and sequencingAI use cases across HR, with a focus on areas such as workforce planning, retention risk, talent forecasting, organizational health, and manager effectiveness
• Defining the product requirements and business outcomes forAI-powered decision support toolsthat give managers and HR leaders proactive, forward-looking intelligence
• Partnering withData Engineering and People Analyticsto ensure that predictive models and intelligent systems are built to the right specifications and deployed in ways that drive real behavioral change
• Championing the integration of AI capabilities intoexisting HR workflows-so that intelligence feels like a natural part of how work gets done, not a separate tool that requires extra effort to access

Raise the Bar for Data-Driven Decision-Making Across HR

Operate as a standards-setter and thought leader for how HR uses data-without owning execution or centralizing work. This includes:

• Acting as atranslator between the business and technical teams- turning functional pain points and strategic priorities into clear product requirements that Data Engineering, People Analytics, and HR Technology can execute against
• Helping HR functional teamsraise their own data fluencyand adopt new products and capabilities as they are built
• Defining what "good" looks like formetrics definitions, analytical rigor, experimentation design, and data storytellingacross HR products and teams

Qualifications

Must Have:

• 10+ years in product management, data products, or analytics-with demonstrated ownership of complex, cross-functional initiatives as an individual contributor
• Proven ability toinfluence-driving alignment and action across engineering, analytics, and business teams through clarity, credibility, and structured thinking
• Deep analytical fluency: comfortable with data architecture concepts, metrics frameworks, semantic layers, and model-driven products
• Track record of translating ambiguous, high-stakes business problems into structured product strategies and roadmaps
• Experience partnering withboth technical teams and business stakeholders-able to move fluidly between conversations about data pipelines and conversations about talent strategy

Strongly Preferred:

• Experience building and scalingdata platforms, analytics products, or AI-enabled toolsin an enterprise environment
• Background inHR technology, people analytics, or workforce intelligence
• Familiarity withHCM platforms, data governance practices, and analytics semantic layer concepts
• Experience applyingresponsible AI principles, including bias assessment, model explainability, and data privacy in a people data context
• Experience working in or closely withHR functional domainssuch as Talent Acquisition, Talent Management, or Total Rewards