Data Product Owner: AI-Driven Analytics Leader

Marathon Health

United States

Remote

USD 110,000 - 140,000

Full time

6 days ago

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Job summary

Marathon Health is seeking a Data Product Owner to lead cross-functional scrum teams delivering outcome-based data solutions across internal and external stakeholders. The role encompasses defining problems, metrics, and acceptance criteria, with delivery options including Tableau, Snowflake Cortex, Python, and Streamlit, guided by a semantic model for reusable, governed data layers.

The ideal candidate will leverage AI to accelerate discovery, prototyping, and iteration, build self-service

Qualifications

Bachelor’s degree in a relevant field plus a minimum of 7 years proven experience as a Product Owner, Product Manager, or similar role delivering data, analytics, or BI products.
Demonstrated success leading Agile / Scrum teams, ideally more than one team concurrently.
Strong background in data and analytics, with the ability to reason about data products end to end (source to consumption).
Experience in healthcare, population health, benefits administration, or related regulated industries preferred.
Experience working with data governance, data quality, and master data initiatives preferred.

Responsibilities

Own the product vision, roadmap, and prioritized backlog for a portfolio of outcome-based data solutions serving internal and external stakeholders.
Lead multiple scrum teams day to day — including backlog grooming, sprint planning, prioritization, and removing blockers — while keeping teams focused on measurable outcomes over outputs.
Take direction from the Director of Business Intelligence on analytics and reporting outcomes, and from the Technical Product Manager on technical and product execution, reconciling their input into a single prioritized backlog.
Proactively surface conflicts in priority or direction early, and drive them to resolution rather than letting teams stall.
Translate stakeholder goals into clearly defined problems, success metrics, and acceptance criteria, and validate that delivered solutions actually move those metrics.
Select the right delivery approach for each problem (e.g., Tableau, Snowflake / Cortex agents, Python / Streamlit portals) based on the outcome and the audience, rather than defaulting to a preferred technology or pattern.
Leverage AI and modern tooling to accelerate discovery, prototyping, development, and iteration across the teams.
Build and steward foundational, reusable components — especially the semantic model — so that stakeholders and other teams can reliably build their own solutions against a trusted, governed layer.
Establish and support self-service enablement, including documentation, patterns, and guardrails, so users can create their own solutions with confidence.
Partner closely with Product leaders and internal stakeholders who depend on these solutions, keeping all parties aligned on priorities, tradeoffs, and delivery status.
Anticipate and absorb change from both Technology and Product, adjusting priorities and communicating impacts without losing delivery momentum.
Ensure solutions meet quality, security, and compliance expectations appropriate to internal and external use.
Track adoption, usage, and business impact, and use those insights to continuously refine the portfolio.
Define success metrics and business outcomes for all major initiatives and hold teams accountable for measuring realized value after delivery.
Ensure solutions follow established release, change management, and production readiness processes.