Data Product Manager

Job Title: Data Product Manager
Location: Hybrid – St. Paul, MN
Work Location: 444 Lafayette Rd N, St. Paul, MN 55155
Duration: September 2026 – August 2027
 

Position Overview

We are seeking an experienced Data Product Manager to lead the strategy, roadmap, and execution of enterprise data products supporting the State Department

The Data Product Manager will help transform organizational data into trusted, reusable, governed, scalable, and high-value data products, including curated datasets, analytics platforms, data services, and modern data infrastructure.

This position will work closely with Data Engineering, Data Science, Analytics, Enterprise Architecture, Business, Policy, and Administration teams to modernize data practices and transition from legacy mainframe systems toward modern data lakes, data platforms, data curation, and analytics capabilities.

The ideal candidate combines strong product management leadership with a deep understanding of data architecture, data engineering, analytics, data governance, and modern data platforms. The candidate must be comfortable translating complex technical concepts into clear business requirements and driving data products through their complete lifecycle.

Key Responsibilities

Data Product Strategy & Vision

• Define and communicate the vision, strategy, and roadmap for enterprise data products.
• Identify high-value data opportunities by analyzing the existing data landscape, business challenges, and organizational needs.
• Align data product strategy with business priorities and long-term enterprise data architecture.
• Partner with Enterprise Architecture and technical stakeholders to establish sustainable data strategies.
• Connect data capabilities to measurable business outcomes.
• Establish product priorities and use metrics to guide product evolution.

Data Product Development

• Lead the complete lifecycle of data products from ideation and requirements through design, development, testing, deployment, monitoring, iteration, and deprecation.
• Work closely with Data Engineers and Data Scientists to develop scalable data pipelines, models, platforms, and data services.
• Ensure data products meet requirements for data quality, governance, security, lineage, metadata, documentation, and scalability.
• Translate business rules and requirements into data transformations, metadata, models, and domain-specific data rules.
• Demonstrate a strong understanding of data architecture, data modeling, ETL/ELT, pipelines, data warehousing, and data lakes.
• Ensure delivered data products are reliable, reusable, governed, and consumable across the organization.

Stakeholder & Cross-Functional Management

• Serve as the primary liaison between technical teams and DCYF business stakeholders.
• Collaborate with Data Engineering, Data Science, Analytics, Architecture, Policy, and Administration teams.
• Communicate product vision, value, roadmap, use cases, and progress to leadership and stakeholders.
• Gather, analyze, and prioritize competing business and technical requirements.
• Facilitate alignment between business objectives and technical implementation.
• Effectively communicate complex technical concepts in business-friendly language.

Analytics, Insights & Measurement

• Define product success metrics, KPIs, adoption measures, and performance indicators.
• Partner with analytics teams to develop dashboards, reporting frameworks, and actionable insights.
• Ensure data products support effective decision-making and operational efficiency.
• Monitor product adoption and performance and use insights to continuously improve products.

Data Governance, Compliance & Responsible Data Use

• Promote strong data governance, privacy, security, quality, lineage, and metadata practices.
• Ensure data products comply with organizational policies and applicable regulatory requirements.
• Understand data-sharing constraints, data-sharing agreements, and enterprise data access requirements.
• Advocate for responsible and ethical use of data and AI within the human services environment.
• Support appropriate handling and use of sensitive organizational data.

Knowledge Transfer

• Provide knowledge transfer to internal teams and stakeholders.
• Develop and maintain appropriate product, technical, business, and data documentation.
• Help mature data product management practices and organizational data capabilities.

Required / Desired Qualifications

• 4–7+ years of experience in Data Product Management, Data Management, Data Analytics, Data Engineering, or a related field.
• Demonstrated experience leading data products, data platforms, or analytics products.
• Strong product leadership skills with the ability to operate effectively in ambiguous and complex environments.
• Strong understanding of:
  • Data architecture
  • Data modeling
  • Data pipelines
  • ETL/ELT
  • Data warehousing
  • Data lakes
  • Metadata
  • Data governance
  • Data quality and lineage
• Proven experience collaborating with Data Architects, Data Engineers, Data Scientists, and Analytics teams.
• Strong ability to translate technical requirements into business-friendly language and business needs into technical requirements.
• Excellent stakeholder management, communication, prioritization, facilitation, and problem-solving skills.
• Experience with modern analytics and data tools such as Databricks, dbt, Looker, Tableau, Power BI, and/or Google Analytics.
• Strong SQL experience and understanding of data transformation and querying.
• Significant hands-on or product-level experience with Databricks.
• Familiarity with Python and Java.
• Experience working with data lakes and modern data architectures.
• Experience building or managing internal data platforms, developer-facing products, or enterprise data services.
• Experience implementing or modernizing data architecture within a large organization.
• Understanding of enterprise data-sharing constraints and data-sharing agreements.
• Experience working in a highly regulated industry involving statistical analysis, reporting, governance, privacy, or compliance is highly desirable.
• Ability to work effectively with both technical and non-technical stakeholders.

Key Technical Skills

Data & Analytics: SQL, Data Analytics, Data Modeling, Data Warehousing, Data Lakes, ETL/ELT, Data Pipelines, Metadata, Data Governance, Data Quality, Data Lineage

Platforms & Tools: Databricks, dbt, Looker, Tableau, Power BI, Google Analytics

Programming: Python, Java

Product Management: Data Product Strategy, Product Roadmaps, Product Lifecycle Management, Requirements Gathering, Backlog Prioritization, Product Metrics, KPI Development

Architecture: Modern Data Architecture, Enterprise Data Architecture, Data Platform Architecture, Data Lake Architecture

Governance: Data Privacy, Data Security, Data Sharing Agreements, Regulatory Compliance, Responsible AI, Data Governance

Ideal Candidate Profile

The ideal candidate is a Data Product Manager with strong technical depth who can bridge the gap between business stakeholders and technical data teams.

The candidate should be able to understand how data is ingested, transformed, stored, governed, modeled, analyzed, and consumed, while simultaneously managing product strategy, stakeholder expectations, prioritization, and business outcomes.

Experience with Databricks + SQL + data lakes/data pipelines + modern data architecture + product management will be particularly valuable for this opportunity.