Analytics Engineer II

Whoop•Boston, MA

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 hiring an Analytics Engineer II to join our Analytics Engineering team. This role will focus on building and maintaining scalable, well-tested data models in dbt that power analytics and reporting across the company. You will work closely with Analytics and Data Engineering to expand and improve WHOOP's data infrastructure as we scale — executing on architectural initiatives set by senior team members while developing deep expertise in our data platform.

Requirements

2-4 years of experience in analytics engineering, data engineering, or business intelligence with hands-on dbt experience.
Strong SQL skills, particularly within Snowflake or comparable cloud data warehouses.
Experience with dbt in a production environment — building models, writing tests, maintaining documentation, and working across multiple environments.
Working knowledge of Git and version control workflows (branching, pull requests, code review).
Familiarity with dimensional data modeling concepts and schema design best practices.
Detail-oriented with the ability to follow established patterns and execute on well-defined technical plans.

Nice To Haves

Experience with Snowflake-specific features
Familiarity with BI tools such as Sigma.
Knowledge of Python for data transformation or automation tasks.
Experience with CI/CD pipelines for dbt (e.g., CircleCI, GitHub Actions, dbt Cloud).
Prior exposure to subscription/SaaS business models or consumer health/fitness data.

Responsibilities

Develop, optimize, and maintain dbt models across WHOOP's Snowflake data warehouse, following established patterns and coding standards.
Execute on database restructuring initiatives — migrating domain-specific schemas into purpose-built databases to improve data governance and role-based access control.
Implement and maintain data quality tests and documentation within dbt, ensuring models are reliable, well-documented, and discoverable.
Partner with Analytics teams to understand data requirements and translate them into performant, accurate transformation logic.
Collaborate with Data Engineering on data ingestion improvements, source freshness monitoring, and warehouse optimization.
Support the team's data governance goals, including maintaining consistent naming conventions, enforcing data contracts, and managing database roles and grants.
Apply software engineering best practices to analytics code, including version control (Git), code review, testing, and continuous integration.
Build domain expertise in assigned business areas, becoming the technical point of contact for data models and workflows in those domains.