Data Product Manager - St. Paul, MN - Remote or Hybrid, local preference

Data Product Manager - St. Paul, MN

Iceberg's direct St. Paul, MN Client seeks an experienced Data Product Manager.
Iceberg seeks one full-time Data Product Manager who will be responsible for supporting significant system changes to turn the Client’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. The Product Manager will guide products through the full lifecycle—from ideation and design to development, deployment, monitoring, and deprecation.

The work is expected to be done remotely or hybrid with some onsite requested as needed in St. Paul MN. The resource must be located within the borders of the United States and be available for work during regular business hours of 7:00 a.m. - 6:00 p.m. Central Time, Monday through Friday.

Project Schedule
Anticipated Project Start Date: September 2026
Anticipated End Date: August 2027

Requirements

Sample Tasks

·        Product Strategy & Vision:

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

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

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

o   Connect data capabilities to business outcomes
and organize efforts to achieve the business outcomes.

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

·        Data Product Development:

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

o   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.

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

o   Ensures data products are reliable, governed,
and scalable.

·        Interested Parties Management:

o   Serve as the primary liaison between technical
teams and business partners.

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

o   Prioritize incoming requests and balance
competing needs across teams.

·        Analytics, Insights & Measurement:

o   Define success metrics and measure product
performance and adoption.

o   Ensure data products deliver actionable insights
and support decision making.

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

·        Governance, Compliance & Ethical Data Use:

o   Uphold data governance, privacy, and ethical AI
standards.

o   Ensure compliance with regulatory and
organizational data policies.

o   Advocate for responsible data use across the
human services space served by and supported by Client. 

·        Provide knowledge transfer

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.