Manager, Analytics Engineering

Whoop•Boston, MA

19h•$150,000 - $215,000

About The Position

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. We are looking for an Analytics Engineering Manager to lead and scale our Analytics Engineering team. Analytics Engineers at WHOOP embed closely with our Analytics teams and build the trusted transformation layer that turns raw data into reliable, analytics-ready models. In this role, you will lead the team responsible for the quality, scalability, and usability of that data foundation. You will partner closely with Analytics to understand how the business needs to use data, and with Data Engineering and Analytics Platform to ensure the underlying pipelines, infrastructure, and tooling support those needs. You will hire and develop a high-performing team, establish strong engineering practices, and stay close enough to the technical work to provide direction and maintain a high quality bar. As WHOOP expands how it uses AI across the organization, you will also help ensure our analytical data is well-modeled, documented, governed, and structured for both human and AI-powered consumption.

Requirements

5+ years of experience in analytics engineering, data engineering, or a related field, including experience leading and managing technical teams.
Deep expertise in SQL, dbt, and modern cloud data warehouses such as Snowflake, with a strong understanding of dimensional modeling and analytical data architecture.
Experience building and maintaining large-scale analytical data models used by multiple teams and business functions.
Strong understanding of software-engineering practices applied to analytics, including version control, automated testing, CI/CD, code review, documentation, observability, and data quality.
Experience coaching and developing a team of engineers, setting clear expectations, and creating a high-performing and inclusive team environment.
Demonstrated ability to work across Analytics, Data Engineering, and business teams, translating ambiguous requirements into clear technical direction.
Excellent written and verbal communication skills, with the ability to explain technical decisions, trade-offs, and risks to both technical and non-technical audiences.

Nice To Haves

Experience designing or contributing to a semantic or metrics layer, such as Snowflake Semantic Views, dbt Semantic Layer, or similar technologies.
Experience building data foundations for AI-powered analytics, agents, or natural-language data experiences.
Experience with Sigma or comparable business intelligence and analytics tooling.
Experience with data observability, lineage, governance, or data-contract frameworks.
Experience in subscription/DTC commerce, health and wellness, or consumer technology.

Responsibilities

Lead and develop the Analytics Engineering team, including hiring, coaching, performance management, career development, and creating an environment where engineers can do their best work.
Own the health and evolution of WHOOP's analytics transformation layer, ensuring analytical models are reliable, maintainable, performant, well-tested, and appropriately documented.
Partner closely with Analytics teams to translate analytical and business requirements into scalable data models that support reporting, experimentation, forecasting, and decision-making.
Serve as a key interface between Analytics and Data Engineering, aligning on source data requirements, data contracts, pipeline changes, migrations, and the architecture needed to make trusted data available to analytical consumers.
Establish and uphold Analytics Engineering standards for dbt development, testing, documentation, lineage, model design, code review, and data-quality practices, in partnership with Analytics Platform.
Set priorities and drive execution for the team, translating broader DAA and company goals into a clear roadmap, managing trade-offs, and proactively communicating risks, dependencies, and progress.
Build AI leverage into the team's work, both by improving how Analytics Engineers use AI in development workflows and by making analytical data easier for AI-powered tools and agents to consume safely and reliably.
Stay current on analytics engineering practices and emerging technologies, evaluating opportunities to improve quality, velocity, and scalability.