Data Modernization Jobs in Minnesota (NOW HIRING)

We are looking for an experienced Data Product Manager to lead the strategy, roadmap, and delivery of enterprise data products supporting a major data modernization initiative for the client. This individual will work across data engineering, data science, analytics, architecture, and business teams to transform legacy data environments into scalable, governed, reusable data products including curated datasets, analytics platforms, data lakes, pipelines, and supporting infrastructure. The position will play a key role in moving the client from legacy mainframe-based systems toward modern data lake and data curation platforms.

This is a hybrid position based in St. Paul, Minnesota. Candidates must be able to work onsite as required at the client's St. Paul location. Local Minnesota/Twin Cities candidates will be favored, and relocation expenses will not be provided. Work will generally occur during regular Central Time business hours.

We are looking for an experienced Data Product Manager to lead the strategy, roadmap, and delivery of enterprise data products supporting a major data modernization initiative for the client. This individual will work across data engineering, data science, analytics, architecture, and business teams to transform legacy data environments into scalable, governed, reusable data products including curated datasets, analytics platforms, data lakes, pipelines, and supporting infrastructure. The position will play a key role in moving the client from legacy mainframe-based systems toward modern data lake and data curation platforms.

This is a hybrid position based in St. Paul, Minnesota. Candidates must be able to work onsite as required at the client's St. Paul location. Local Minnesota/Twin Cities candidates will be favored, and relocation expenses will not be provided. Work will generally occur during regular Central Time business hours.

No C2C, recruiters need not inquire.

• Define and maintain the vision, strategy, and roadmap for enterprise data products, including data platforms, curated datasets, analytics solutions, data services, and supporting infrastructure.
• Identify high-value data opportunities by evaluating business requirements, organizational priorities, pain points, data availability, and the existing data landscape.
• Lead modernization efforts involving migration from legacy mainframe systems to modern data lakes, data curation platforms, and scalable data architectures.
• Align data product strategy with enterprise architecture, business objectives, and long-term technology strategy.
• Lead the complete product lifecycle from discovery and requirements through design, development, testing, deployment, adoption, monitoring, enhancement, and eventual deprecation.
• Partner closely with data engineers, data architects, data scientists, analysts, and business stakeholders to develop scalable data pipelines, models, transformations, and services.
• Translate complex business requirements and business rules into data requirements, transformations, metadata, domain rules, and technical product capabilities.
• Ensure data products meet standards for quality, scalability, governance, lineage, metadata, documentation, privacy, and reliability.
• Serve as a primary liaison between technical teams and business stakeholders.
• Manage product priorities, roadmaps, competing stakeholder needs, dependencies, risks, and delivery expectations.
• Define KPIs, adoption metrics, performance measures, and success criteria for data products.
• Partner with analytics and reporting teams to develop dashboards, reporting frameworks, and actionable business insights.
• Support enterprise data governance, data-sharing agreements, privacy requirements, regulatory compliance, and responsible data use.
• Promote appropriate and ethical use of data within highly regulated environments.
• Coordinate knowledge transfer and documentation to support long-term ownership and sustainability of solutions.
• Approximately 4–7+ years of experience in data product management, data management, data analytics, data engineering, or closely related disciplines.
• Demonstrated experience leading data products through the complete product lifecycle .
• Strong product leadership skills and ability to lead initiatives where requirements may initially be ambiguous.
• Strong understanding of:
• Data pipelines
• ETL/ELT
• Data lakes
• Data warehouses
• Data modeling
• Metadata
• Data lineage
• Data governance
• Data quality
• Modern data architectures
• Significant hands-on or product-level experience with Databricks.
• Experience working closely with data architecture, data engineering, analytics, and data science teams.
• Experience implementing or supporting modern enterprise data architectures and modernization initiatives.
• Strong SQL knowledge and experience working with enterprise datasets.
• Experience defining requirements for scalable pipelines, curated datasets, and data services.
• Ability to translate highly technical data concepts into clear business language and business requirements into technical deliverables.
• Strong stakeholder management, communication, prioritization, and facilitation skills.
• Experience with one or more analytics/data tools such as dbt, Tableau, Power BI, Looker, or Google Analytics.
• Experience with large organizational data-sharing requirements, governance frameworks, and data-sharing agreements.
• Familiarity with Python and Java.
• Experience developing internal platforms, enterprise data products, or developer-facing products.
• Experience operating within a highly regulated environment, including statistical analysis, reporting, data privacy, or compliance requirements.
• Ability to work collaboratively across technical, business, policy, and administrative teams.
  

• Previous experience supporting Minnesota state government, MNIT, or other large public-sector organizations.
• Experience supporting human services, health and human services, children/family services, or similarly regulated government programs.
• Experience modernizing legacy or mainframe-based enterprise data environments.
• Strong experience with Databricks-based data lake or lakehouse architectures.
• Experience establishing reusable, governed enterprise data products rather than developing one-off reporting solutions.
• Experience working with enterprise architecture teams on modernization roadmaps.