At WHOOP, we are on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.
WHOOP is seeking an experienced Data Engineer who thrives on innovation and takes ownership of building and evolving data systems at scale. In this role, you will design, build, and optimize scalable data pipelines and platforms that power our data driven insights. You will play a key role in shaping robust ELT architectures, improving reliability and performance, and influencing technical direction across the data platform. With a strong focus on modern AWS infrastructure and tooling such as Snowflake, DBT, Kafka, and Spark, you will help elevate our analytical and operational capabilities. If you are excited about using AI to improve developer productivity and drive meaningful impact, we want you to join our team.
RESPONSIBILITIES:
Design, build, and operate scalable ELT pipelines using Python and PySpark, with a focus on reliability, performance, and maintainability
Own and improve batch and streaming data systems using Spark and Kafka, including monitoring and resolving production data issues
Develop and optimize Snowflake data models and DBT transformations to support analytics, experimentation, and trusted metrics
Partner with data scientists, analysts, and product teams to translate business requirements into well-designed data solutions
Contribute to the evolution of the data platform by improving observability, data quality, and engineering best practices
Leverage AI tools to accelerate development, improve code quality, and automate repetitive data engineering workflows
QUALIFICATIONS:
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
3-5 years of professional experience building and operating ETL/ELT pipelines in production environments
Strong proficiency in SQL and hands-on experience with modern data warehousing concepts and dimensional modeling
Professional experience using Python for data engineering, including writing clean, testable, and reusable code
Experience with DBT for data modeling, testing, and documentation is preferred
Experience with Spark and Kafka for batch or streaming data processing is preferred
Strong problem-solving skills, clear communication, and the ability to work independently while collaborating in an agile environment
Comfort using AI tools such as Copilot or ChatGPT to improve efficiency throughout the software development lifecycle
This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.
Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.
WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibilityThe WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.
At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success.
The U.S. base salary range for this full-time position is $125,000 - $175,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training. In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.
