Data Product Manager

Sample Tasks

• 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 Qualifications

• 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.