Data Product Manager

Sample Tasks

Product Strategy & Vision:

oDefine the vision and roadmap for data products (e.g., data platforms, analytics tools, ML infrastructure).

oIdentify high value opportunities by investigating the data landscape, pain points, and business needs.

oAlign data product strategy with organizational priorities and long-term data architecture, in partnership with the Enterprise Architecture team and various interested parties.

oConnect data capabilities to business outcomes and organize efforts to achieve the business outcomes.

oAlign engineering, analytics, and business teams. Uses metrics to guide prioritization and product evolution.

Data Product Development:

oLead the end-to-end lifecycle of data products: requirements, design, development, testing, launch, and iteration.

oPartner with data engineers and data scientists to build scalable pipelines, models, and data services. Ensure data quality, governance, lineage, and documentation standards are met.

oTranslate business logic into data transformations, metadata, and domain specific rules. Skilled in or adept at data architecture, modeling, and pipelines.

oEnsures data products are reliable, governed, and scalable.

Interested Parties Management:

oServe as the primary liaison between technical teams at Minnesota IT Services (MNIT) and business partners across DCYF.

oCommunicate product value, roadmap, and use cases to leadership and cross-functional teams.

oPrioritize incoming requests and balance competing needs across teams.

Analytics, Insights & Measurement:

oDefine success metrics and measure product performance and adoption.

oEnsure data products deliver actionable insights and support decision making.

oPartner with analytics teams to design dashboards, KPIs, and reporting frameworks.

Governance, Compliance & Ethical Data Use:

oUphold data governance, privacy, and ethical AI standards.

oEnsure compliance with regulatory and organizational data policies.

oAdvocate 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.