Core Responsibilities

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

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

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

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