Lead Data Product Manager

We're looking for a Lead Data Product Manager to own the data products that power how we run the business. This greenfield opportunity involves building foundations, earning stakeholder trust, and shipping data products that decisions rely on. You'll partner directly with Finance and People & Culture leaders to translate their priorities into well‑scoped data products, working alongside Analytics and Data Engineering to bring them to life.

You own requirements, quality, and adoption — not just delivery. Every data product we build must support multiple consumers: a Finance analyst in a BI dashboard, an AI agent answering a business question, or a downstream application. Consistent definitions, reliable outputs, and documented semantics are the standard. You’ll also use AI in your own workflow to improve how you synthesize requirements, analyze stakeholder input, and accelerate product development.

This role reports to the VP, Data & Analytics.

• Define and own the data product roadmap.
• Partner with Finance and People & Culture leaders to identify data gaps, prioritize opportunities, and build a roadmap that delivers measurable value.
• Design data products that support multiple consumption patterns, including BI tools, AI‑enabled experiences, and business applications.
• Use data usage patterns—dashboard activity, query volume, and recurring reporting requests—to proactively identify unmet needs.
• Translate business objectives into clear requirements, challenging assumptions early to ensure teams are solving the right problems.
• Drive cross‑functional alignment by serving as a strategic data partner across business stakeholders, Analytics, and Data Engineering teams.
• Facilitate collaboration across functions while identifying shared data needs and reducing siloed solutions.
• Build prioritization frameworks that balance competing needs and provide transparency around trade‑offs.
• Communicate effectively with technical teams, business leaders, and executives.
• Lead data product development by owning the full product lifecycle across multiple Finance and People & Culture initiatives, from discovery through Alpha, Beta, and GA releases.
• Design user‑focused data experiences that balance technical requirements with stakeholder needs.
• Define and maintain semantic contracts, including field definitions, metric logic, and data grain specifications.
• Identify reusable data assets and scalable models that support multiple downstream use cases.
• Maintain a high bar for accuracy, freshness, and reliability while proactively resolving issues before they impact users or AI workflows.
• Use AI tooling to improve product workflows, including requirements synthesis, AI‑assisted SQL validation, catalog generation, and data pattern analysis.
• Drive adoption and measure impact by defining success metrics for each data product and measuring adoption and business impact over time.
• Develop a deep understanding of end‑user workflows and leverage personas to create solutions that address real business needs.
• Gather stakeholder feedback after launch and continuously improve products based on usage and outcomes.
• Ensure data products are structured and documented to support trusted AI‑enabled experiences.
• Build in partnership with engineering, serving as the product counterpart to Data and Analytics Engineering—owning the “what” and “why” while partnering on the “how.”
• Collaborate on semantic layer design and dbt transformations to create consistent metric definitions across BI tools, LLMs, reverse ETL pipelines, and applications.
• Identify opportunities to improve ingestion, transformation, and delivery processes while advocating for investments that increase reliability and scalability.
• Lead agile delivery cycles by translating roadmap priorities into initiatives and epics with clear acceptance criteria.
• Enforce data governance & quality, lead data catalog efforts for your product area, and champion data quality frameworks and governance standards around access controls, retention, and responsible data usage.
• Partner across teams to solve systemic data challenges rather than shifting complexity downstream.
• Foster a data‑driven culture by…