VP of Data, MyHealthTeam
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Tech stack
Job description
The VP of Data will lead the data organization encompassing engineering and analytics, drive strategic insights, improve operational excellence, and integrate AI into workflows while ensuring data governance. The summary above was generated by AI
Join us to redefine the patient experience
MyHealthTeam builds communities for people living with chronic and rare conditions. We reach millions of people each month, and we're investing deeply in AI to help members find the right support, content, and recommendations - safely, responsibly, and at scale.
We're looking for a VP of Data to unify and elevate our data function into a strategic, executive-led discipline. This leader will own data engineering, analytics, governance, and AI data readiness end-to-end - building the foundation that allows AI and LLM tools to become part of everyday workflows across the company.
What you'll do
- Lead the unified data organization spanning Data Engineering (ETL, pipelines, warehouse, modeling), Analytics (enterprise reporting, business intelligence, predictive modeling), data governance and standards, and AI data readiness and semantic architecture.
- Build a multiyear data architecture roadmap that aligns data modeling with enterprise consumption needs and ensures structured data is ready to power AI use cases.
- Strengthen analytics as a strategic partner to the business. Drive proactive insight generation, strategic framing of data for leadership, and enterprise-level thinking about how data creates competitive advantage.
- Be a hands-on technical leader who can go deep on architecture, data modeling, and pipeline design when needed, while also excelling as a team manager, mentor, and executive partner.
- Define how AI and LLM tools become part of everyday workflows. Work in partnership with business users to define and implement the pattern for delivering refreshable, governed datasets into AI assistant environments to enable business teams to self-serve from current data without manual uploads or file management.
- Systematically organize key operating data sources into structured pipelines that allow LLM tools to safely and reliably assist employees. Data sources can be as diverse as product databases, Google Analytics, ad serving platforms, paid media buying platforms, and client performance KPIs.
- Establish governance, consistency, and clarity across data systems, ensuring data quality, accessibility, and auditability at scale.
- Drive operational excellence in data delivery. Improve service levels to business teams, clarity of prioritization, transparency in timelines, and consistency in delivery through process redesign, project management discipline, and tooling improvements.
- Partner closely with Engineering, Product, and operating leaders to ensure schema design aligns with business usage and LLM readiness is built into data architecture from the ground up.
Examples of the Outcomes You'll Drive
- A unified enterprise data model with systematic ingestion of key operating data into AI-ready structures
- Disciplined, cross-functional LLM usage powered by governed, refreshable, internal datasets
- Automated client report generation that improves efficiency, consistency, and client responsiveness
- LTV/CE mix analysis that supports proactive account planning and better commercial prioritization
- A content intelligence engine that identifies the highest-value SEO, CRM, and editorial opportunities from unified performance, audience, and content data
- Marketing optimization including channel mix recommendations and improved attribution accuracy
Requirements
- 12+ years of experience in data engineering, analytics, or related fields, with 5+ years in senior leadership roles managing cross-functional data teams
- Proven track record building and scaling unified data organizations (engineering and analytics) in a product-driven company
- Deep expertise in modern data architecture: warehousing, ETL/ELT pipelines, data modeling, and governance at scale
- Strong understanding of how structured data enables AI/LLM use cases, including semantic layers, retrieval systems, and data readiness patterns
- Experience defining and executing multiyear data strategy and roadmaps at the executive level
- Demonstrated ability to elevate analytics from reactive reporting to proactive, strategic insight generation
- Strong executive presence and stakeholder instincts: you can partner with senior leaders across the business and translate data capabilities into competitive advantage
- Experience driving operational excellence in data delivery - improving responsiveness, prioritization, and service levels for business teams, * Experience in healthcare, health tech, or regulated environments (HIPAA/PHI familiarity a plus)
- Hands-on familiarity with LLM and AI tooling and an appreciation for how emerging AI capabilities reshape data strategy
- Experience with experimentation platforms, causal inference, and measurement frameworks
- Background in consumer products with large-scale behavioral, text, or clickstream data
- Demonstrated ability to drive AI and data fluency across nontechnical teams, with a track record of both delivery and adoption
- Ability to meet in person in our San Francisco office two days per week, or if located remotely, travel up to quarterly for onsites
Some Tools We Use
- Databricks/Spark for distributed processing
- Redshift and BI tools (Looker/Tableau) for analytics
- Terraform for infrastructure-as-code, Airflow for orchestration, GitHub Actions for CI/CD
- AWS (including Bedrock) and a mix of private and open-weight models
- Mongo/product databases, GA4, ad serving and paid media platforms
- AI-assisted coding and workflow tools (e.g., Cursor, Copilot, and Claude/OpenAI tooling)