Technical Data Product Manager

Core Responsibilities

Product Strategy and Planning

• Assess current state rapidly: Work with data engineering and architecture teams to understand complex legacy landscapes—what exists, where critical information lives, and how systems actually work
• Contribute to future state vision: Partner with VP Data Engineering, CTO, and architecture leads to shape target data architectures, canonical models, and platform capabilities that will scale to support product teams
• Develop product roadmap: Translate business priorities into data product requirements, working with technical leadership to sequence initiatives and balance migration work, new capabilities, and product enablement
• Support technical evaluations: Contribute product perspective to build vs. buy decisions, technology evaluations (lakehouse formats, real-time processing, AI-powered automation), and architectural choices
• Define and track success metrics: Establish product-level OKRs, track adoption across product teams, and communicate progress to stakeholders

Execution and Coordination

• Drive cross-functional delivery: Coordinate data initiatives from requirements through production, working across data engineering, data science, platform engineering, and product teams
• Unblock relentlessly: Identify and resolve dependencies, bottlenecks, and blockers before they slow down team velocity
• Navigate complexity: Find critical information scattered across legacy platforms, undocumented systems, and tribal knowledge; synthesize insights and create clarity
• Facilitate decisions: Build consensus across teams with competing priorities and different technical opinions
• Leverage AI extensively: Use LLMs and AI-powered tools to accelerate analysis, documentation, SQL generation, information synthesis, and decision-making
• Establish lightweight visibility: Create metrics, dashboards, and reporting that provide insight without creating overhead

Technical Collaboration and Product Enablement

• Partner with technical leadership: Work closely with data engineering, data science, and architecture leads—contributing product perspective while respecting their technical expertise and domain ownership
• Translate requirements: Convert product team needs into clear technical requirements that engineering teams can execute against
• Enable product teams: Ensure downstream product teams can successfully consume data platform capabilities through clear interfaces, documentation, and support
• Participate in technical discussions: Engage substantively in reviews of ETL pipelines, data models, distributed architectures, and platform decisions
• Bridge stakeholders: Translate complex technical concepts into business value for executives and product teams; bring business context to technical discussions

What You'll Work On

• Data consolidation and unification across legacy platforms—working with engineering teams to coordinate migrations to unified infrastructure while maintaining production stability and enabling parallel product development
• Canonical data model development—collaborating with data engineering and data science leadership to define product requirements for production-ready models covering medical claims, pharmacy claims, eligibility, and other core healthcare entities
• Platform modernization—contributing product perspective to technical evaluations and roadmaps for lakehouse adoption, OLAP/OLTP separation, real-time processing capabilities, and distributed architecture patterns
• AI-powered automation—partnering with technical teams to evaluate and implement LLM-based approaches that accelerate ETL development, data transformation, and migration workflows
• Data discovery and cataloging—working with engineering to define requirements for capabilities that help teams understand what data exists, where it lives, how to access it, and what it means
• Product team enablement—ensuring downstream product teams can successfully consume data platform capabilities through clear interfaces, comprehensive documentation, and responsive support

Required Qualifications

Experience Requirements

• 10+ years total professional experience
• 5+ years in product management roles
• Prior hands-on experience as data engineer, data scientist, or analytics engineer (required)
• Proven track record shipping data products or platforms used by internal/external teams
• Experience driving execution in matrixed organizations without direct authority
• Demonstrated ability to assess complex technical landscapes and define future-state architectures

Technical Skills (Must-Have)

• SQL (writing and reviewing)
• Data architecture review
• ETL pipelines
• Data models
• Distributed architectures