Summary The Product Manager for Enterprise Data is the single point of product leadership across our four enterprise data teams. The Product Manager owns the portfolio from the business problem to the delivered product. You will work directly with business stakeholders to understand their decisions and pain points, turn them into a prioritized roadmap, and guide delivery with Data Architecture & Engineering, Reporting & Analytics, Data Governance and AI Engineering. The goal is to provide data that is trusted, well documented, and ready for consumption.
This is a product role, not project management or pure engineering role. The role sits as a peer to the four functional data managers as well as three functional managers in AI. This role will report directly to the Director of Enterprise Data & AI.
Key Responsibilities Product ownership and strategy - Define the vision, roadmap, and success metrics for enterprise data.
- Maintain a prioritized backlog, balancing business value, effort, data readiness, and governance requirements.
- Treat data as a product: clear ownership, defined consumers, documented SLAs, versioning, and a lifecycle from discovery to retirement.
Direct stakeholder engagement - Act as the primary point of contact for business leaders and process owners. Run discovery sessions, translate business questions into data requirements, and confirm outcomes after release.
- Communicate roadmap, trade-offs, and results clearly to audiences ranging from analysts to senior executives.
- Build a community of data product users and champions who give regular feedback.
Backlog, prioritization, and delivery - Own the enterprise data product backlog; write and refine epics, user stories, and acceptance criteria with input from the functional teams.
- Run a transparent intake and prioritization process using an agreed framework (for example, value versus effort, WSJF, or business impact scoring).
- Facilitate agile ceremonies such as backlog refinement, sprint and release planning, and reviews in partnership with the functional managers.
- Make and communicate trade-off decisions on scope, priority, and timing, and escalate conflicts to the Director when needed.
- Track delivery against the roadmap and report progress, risks, and dependencies to leadership and the enterprise PMO.
Analytics and visualization - Work with the BI team to deliver curated datasets and dashboards in Qlik Cloud, ensuring metrics are consistent and reusable across tools.
- Define KPIs and metric definitions so that "one number" means the same thing everywhere.
- Track usage and adoption and use the findings to improve or retire products.
Governance and quality - Work with Data Governance and Master Data Management to embed ownership, stewardship, data quality rules, access controls, and lineage into each product.
- Ensure data products align with company's AI and data governance policies and risk classification standards.
Delivery leadership - Partner with engineers, architects, and analysts through agile delivery. Write clear user stories and acceptance criteria and remove blockers.
- Measure and report product outcomes: adoption, time-to-insight, data quality, and business impact.
Operating model and team growth - Establish product management practices, templates, and rituals for Enterprise Data & AI Services.
- Help define the future Product Owner model by business domain, and mentor Product Owners once they are in place.
- Continuously improve how the data organization receives, prioritizes, and delivers work.
Required Qualifications - Bachelor's degree in Information Systems, Data Science, Business, Engineering, or a related field, or equivalent experience.
- 5+ years in data, analytics, or product roles, with at least 2 years managing data or analytics products.
- Strong knowledge of Snowflake or similar Data Management platform, including data modeling concepts, views, security and access roles, and performance and cost awareness.
- Familiarity with Snowflake Cortex (Cortex Analyst, Cortex Search, or LLM functions) and how semantic models or views enable governed AI access to data.
- Understanding of what makes data AI-ready: metadata, semantic definitions, data quality, lineage, and governance.
- Experience with Qlik Cloud or a comparable visualization tool (Power BI, Tableau), including working with developers to define requirements and metrics.
- Proven ability to work directly with business stakeholders, facilitating discovery, managing expectations, and driving adoption.
- Strong written and verbal communication, including presenting to senior leaders.
Preferred Qualifications - Experience in manufacturing, supply chain, or another industrial environment.
- Familiarity with ERP data (Infor M3, SAP or similar), master data management, and data governance concepts.
- Experience with dbt, SQL, or Python at a working level a plus (you don't need to be a developer).
- Product management, agile, or data governance certifications (ex. SAFe, CDMP).
- Experience defining data contracts, SLAs, or data mesh and federated ownership models.
Core Competencies Business partnership, product thinking, data literacy, prioritization, influencing without authority, clear communication, and pragmatism about governance and risk.
