VP of Data, MyHealthTeam

Swoop Airlines and Aviation

2h•Hybrid

About The Position

MyHealthTeam is seeking a VP of Data to lead its unified data organization. This role will span Data Engineering (ETL, pipelines, warehouse, modeling), Analytics (enterprise reporting, business intelligence, predictive modeling), data governance and standards, and AI data readiness and semantic architecture. The VP will build a multiyear data architecture roadmap, strengthen analytics as a strategic partner to the business, and drive proactive insight generation. This is a hands-on technical leadership role that requires both deep expertise in data architecture and strong team management skills. The VP will also define how AI and LLM tools become part of everyday workflows, working in partnership with business users to implement patterns for delivering governed datasets into AI assistant environments. The role involves organizing key operating data sources into structured pipelines for LLM tools, establishing governance, consistency, and clarity across data systems, and driving operational excellence in data delivery. Close partnership with Engineering, Product, and operating leaders is essential to ensure schema design aligns with business usage and LLM readiness is built into data architecture.

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

Nice To Haves

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

Responsibilities

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.