The Staff Data Product Manager shapes Michels' enterprise data strategy and leads the effort to define, prioritize, and manage the data products that give the organization self-service access to trusted, reusable data. As a founding leader on the Enterprise Intelligence team, this person guides the standup and evolution of the enterprise data platform, working closely with stakeholders to understand their needs, identify high-value opportunities, and make deliberate investments that balance early wins with the durability, governance, and scalability required for the long term. Ultimately, this role determines how effectively data powers analytics, reporting, operational insight, and AI across the Michels Family of Companies.
Key Responsibilities:- Engage stakeholders across business and technology to surface pain points, clarify needs, and uncover where better data improves decisions.
- Prioritize the highest-value opportunities, deciding which platform capabilities are worth enabling and which products are worth building and why to maximize business value as the platform is established.
- Define curated, intuitive data products, including tables and semantic models, across all domains.
- Translate business needs into actionable stories and acceptance criteria, then guide engineering through development, testing, and delivery.
- Uphold quality and consistency standards so the data assets are documented, dependable, and suited for self-serve consumption.
- Measure adoption and outcomes, using those signals to validate value and guide what comes next.
Qualifications: - Bachelor's degree in Business, Technology, Engineering, Computer Science, a quantitative field, or equivalent practical experience.
- 7+ years of experience in data product management, guiding cross-functional teams to deliver data products on a scalable cloud data platform for enterprise consumption.
- Proven experience defining product roadmaps, requirements, user stories, and success metrics while guiding products or platforms through the full lifecycle from discovery through adoption.
- Strong understanding of data management concepts including data governance, metadata, data quality, data lineage, and the principles required to build trusted and reusable enterprise data assets.
- Exposure to leading cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, BigQuery, or similar technologies.
