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.
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