Data Product Manager

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.

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.

·        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.