Data Product Manager

We are seeking one full-time Data Product Manager who will be responsible for supporting significant system changes to turn the organization’s data into scalable, high value products—such as curated datasets, analytics platforms, and data infrastructure.  This role will lead efforts across data engineering, data science, and business strategy that help mature data practices and teams and drive organizational efforts to shift to new data lakes and data curation platforms from current legacy main frame systems.

At a high level, the resource will lead the strategy, roadmap, and execution of data efforts, enabling better decision making, operational efficiency, and intelligent product experiences. Partnering closely with data engineering, data science, analytics, and business policy and administration, the Data Product Manager will facilitate and help drive the path towards trusted, reusable, governed, and consumable data assets across the enterprise. This role requires strong analytical skills, deep understanding of data systems, and a strong aptitude in translating between technical teams and business understanding, throughout the product lifecycle. Product Managers at DCYF guide products through the full lifecycle—from ideation and design to development, deployment, monitoring, and deprecation.

• Product Strategy & Vision:
  • Define the vision and roadmap for data products (e.g., data platforms, analytics tools, ML infrastructure).
  • Identify high value opportunities by investigating the data landscape, pain points, and business needs.
  • Align data product strategy with organizational priorities and long-term data architecture, in partnership with the Enterprise Architecture team and various interested parties.
  • Connect data capabilities to business outcomes and organize efforts to achieve the business outcomes.
  • Align engineering, analytics, and business teams. Uses metrics to guide prioritization and product evolution.
• Data Product Development:
  • Lead the end-to-end lifecycle of data products: requirements, design, development, testing, launch, and iteration.
  • Partner with data engineers and data scientists to build scalable pipelines, models, and data services. Ensure data quality, governance, lineage, and documentation standards are met.
  • Translate business logic into data transformations, metadata, and domain specific rules.  Skilled in or adept at data architecture, modeling, and pipelines.
  • Ensures data products are reliable, governed, and scalable.
• Interested Parties Management:
  • Serve as the primary liaison between technical teams at Minnesota IT Services (MNIT) and business partners across DCYF.
  • Communicate product value, roadmap, and use cases to leadership and cross-functional teams.
  • Prioritize incoming requests and balance competing needs across teams.
• Analytics, Insights & Measurement:
  • Define success metrics and measure product performance and adoption.
  • Ensure data products deliver actionable insights and support decision making.
  • Partner with analytics teams to design dashboards, KPIs, and reporting frameworks.
• Governance, Compliance & Ethical Data Use:
  • Uphold data governance, privacy, and ethical AI standards.
  • Ensure compliance with regulatory and organizational data policies.
  • Advocate for responsible data use across the human services space served by and supported through DCYF and MNIT DCYF. 
• Provide knowledge transfer

• Desired 4–7 years of experience in Data management, data analytics, data engineering, or related fields.
• Demonstrated Product leadership skills and ability to work in ambiguity.
• Strong understanding of data systems: pipelines, warehousing, modeling, metadata, governance.
• Proficiency collaborating with data Architecture, data engineering and data science teams.
• Ability to translate complex technical concepts into business-friendly language.
• Strong communication, prioritization, and stakeholder management skills.
• Experience with analytics tools (dbt, Looker, Tableau, Power BI, Google Analytics).
• Understanding of large organizational data sharing constraints and data sharing agreements.
• Experience with SQL, data lakes, data and data pipelines / ETL.
• Significant experience with Databricks.
• Familiarity with Java and Python.
• Background in building internal platforms or developer facing products.
• Experience in implementing modern data architectures at an organization.
• Experience in a highly regulated industry performing statistical analysis and reporting.