At WHOOP, we're on a mission to unlock human performance. WHOOP empowers users to perform at a higher level through a deeper understanding of their bodies and daily lives. 

 

We are hiring our first Analytics Engineer to sit at the cross section of Business Analytics and Data Engineering. We are looking for someone who will help us uplevel our data stack to the next level - bringing expertise in all things data, from ingestion and transformation to business intelligence tools. This technical role will work very closely with Data Engineering to define our broader data strategy as well as Analytics, business stakeholders and leadership to understand the business context behind our data.

RESPONSIBILITIES:

• Architect performant, accurate, and scalable data models using dbt and LookML, while enabling analysts and business stakeholders to have a better understanding of WHOOP’s data.
• Set up tests to ensure data quality, supervise daily job execution, and tackle roadblocks to ensure SLAs are met.
• Understand business context and support analysts and business stakeholders in defining source-of-truth Looker Explores and Dashboards, while enabling more of our data to be self-service.
• Document work, prioritize data governance, and provide team members with insight into standard processes.
• Partner closely with Data Engineering on projects such as data ingestion and warehouse optimizations.
• Apply software engineering best practices to Analytics code (e.g. version control, testing, continuous integration).
• Take ownership of dbt, business intelligence tooling and data governance and documentation platforms.

QUALIFICATIONS:

• 4-6 years of experience in an analytics engineering, data engineering, business intelligence and/or analytics
• Expertise in SQL and ETL optimization techniques, especially within Snowflake or similar data warehouses.
• Strong working knowledge of command line and version control
• Experience with building Looker data models (LookML), data pipeline management technologies with dependency checking (i.e. dbt), schema design, and dimensional data modeling.
• Ability to partner with stakeholders to leverage tools, business intuition, and attention to detail for data validation and QA.
• Plus: Knowledge of Python or other scripting languages as well as working with Data Scientists.