Senior Manager, Analytics Platform

Responsibilities

• Own the WHOOP Analytics data model end-to-end, partnering with analysts and business stakeholders to ensure data is modeled to answer key business questions and serve as a single source of truth.
• Hire, manage, coach, and develop a team of Analytics Engineers, driving high performance, career growth, and a culture of technical excellence.
• Serve as the primary conduit between Analytics and Data Platform Engineering, aligning on shared infrastructure, data contracts, and pipeline architecture.
• Own and evolve WHOOP's semantic layer, ensuring consistency and accuracy of key metrics across all downstream tools and consumers.
• Define and enforce coding standards, documentation practices, and development processes (CI/CD, code reviews, testing requirements, data quality monitoring).
• Lead strategic data initiatives spanning multiple teams and business outcomes, from scoping through execution.
• Maintain strong knowledge of industry trends and tooling developments; evaluate and introduce technologies that improve team leverage — including AI-assisted development and data tooling.
• Partner with Analytics leadership and cross-functional teams to build and evolve the data strategy at WHOOP.
• Interface with senior leadership to design, plan, and communicate data strategy and roadmap.

Qualifications

• 7+ years of experience in analytics engineering, data engineering, or a related field, with at least 2 years of people management experience.
• Deep expertise in SQL, dbt, and Snowflake; strong understanding of dimensional modeling and data warehouse design patterns.
• Experience building and leading high-performing data teams, including hiring, mentoring, and setting technical direction.
• Hands-on proficiency with modern data stack tooling (dbt, Snowflake, Sigma or similar BI tools, Git, Python).
• Demonstrated ability to bridge technical and business stakeholders — translating business requirements into data architecture decisions and communicating trade-offs clearly.
• Track record of owning and delivering large-scale data initiatives with measurable business outcomes.
• Excellent communication and stakeholder management skills, particularly in interfacing with senior leadership.

Nice-to-Haves

• Experience implementing or managing a semantic layer (e.g., Snowflake Semantic Views, dbt Semantic Layer, etc.).
• Familiarity with AI/ML data readiness, including building data foundations that support analytics automation and LLM-powered tooling.
• Experience in subscription/DTC commerce, health/wellness, or consumer hardware.
• Background in data governance, data contracts, or metrics layer standardization.