IT Product Manager, Enterprise Data

Summary

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