Senior Data Engineer

Whoop; Boston (MA); On-site; USD 150,000 - 215,000; Full time

Job summary

WHOOP is seeking a Senior Data Engineer to lead design, development, and evolution of data systems powering analytics, experimentation, ML, and business decisions. You will own complex ELT pipelines and data workflows, partnering with Data Science, Analytics, Product, and Engineering to build scalable, well-governed data solutions.

You will mentor engineers, raise engineering standards, and drive data quality, observability, and automation across the data platform.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 5+ years of professional experience designing, building, and operating production data engineering systems.
• Strong proficiency with Python, SQL, and modern ELT development practices, with experience building maintainable, testable, and observable data pipelines.
• Experience designing and optimizing data warehouse solutions in Snowflake or comparable cloud data platforms.
• Experience building and maintaining data transformation frameworks using dbt or similar tooling.
• Experience working with distributed data processing technologies such as Spark, Kafka, or equivalent modern data processing frameworks.
• Demonstrated ability to independently lead complex technical initiatives involving multiple stakeholders from planning through production support.
• Experience mentoring engineers, providing thoughtful technical feedback, and helping raise engineering quality across a team.
• Strong communication skills with the ability to explain technical concepts, navigate tradeoffs, and build alignment across engineering and business stakeholders.
• Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.

Responsibilities

• Lead the design, implementation, and long-term ownership of scalable ELT pipelines and data workflows using Python, PySpark, SQL, and modern cloud technologies.
• Design and optimize data models and Snowflake architectures that enable reliable, performant, and trusted data consumption across analytics, experimentation, and ML use cases.
• Own complex cross-functional data initiatives, translating ambiguous business requirements into maintainable technical solutions while proactively identifying risks, dependencies, and tradeoffs.
• Partner with Product, Engineering, Analytics, Data Science, and Data Platform Engineering to ensure data systems are reliable, scalable, and aligned with evolving business needs.
• Drive improvements in data quality, observability, testing, documentation, and operational excellence, establishing patterns and best practices for the broader team.
• Mentor Data Engineers through design discussions, code reviews, and technical coaching while contributing to hiring, onboarding, and interview processes.

Skills

Python

SQL

ELT development

Data pipelines

Mentoring

Communication

AI tools

Education

Bachelor's degree in CS or related

Tools

Snowflake

dbt

Spark

Kafka

Job description

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.

WHOOP is hiring a Senior Data Engineer to lead the design, development, and evolution of the data systems that power analytics, experimentation, machine learning, and business decision-making across the company. In this role, you will own complex data engineering initiatives from design through production, partnering closely with Data Science, Analytics, Product, and Engineering teams to build reliable, scalable, and well-governed data solutions. As a senior member of the team, you'll raise the technical bar through mentorship, thoughtful engineering practices, and a commitment to continuously improving how Data Engineering operates.

Responsibilities

• Lead the design, implementation, and long-term ownership of scalable ELT pipelines and data workflows using Python, PySpark, SQL, and modern cloud technologies.
• Design and optimize data models and Snowflake architectures that enable reliable, performant, and trusted data consumption across analytics, experimentation, and machine learning use cases.
• Own complex cross-functional data initiatives, translating ambiguous business requirements into maintainable technical solutions while proactively identifying risks, dependencies, and tradeoffs.
• Partner closely with Product, Engineering, Analytics, Data Science, and the Data Platform Engineering team to ensure data systems are reliable, scalable, and aligned with evolving business needs.
• Drive improvements in data quality, observability, testing, documentation, and operational excellence, establishing patterns and best practices that improve the effectiveness of the broader team.
• Mentor Data Engineers through design discussions, code reviews, and technical coaching while contributing meaningfully to hiring, onboarding, and interview processes.
• Contribute to the technical direction of the Data Engineering organization by evaluating new technologies, improving engineering standards, and identifying opportunities to simplify, automate, and scale our data ecosystem.
• Leverage AI tools and automation to accelerate development, improve engineering quality, and increase team productivity while maintaining rigorous validation, security, and engineering standards.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 5+ years of professional experience designing, building, and operating production data engineering systems.
• Strong proficiency with Python, SQL, and modern ELT development practices, with experience building maintainable, testable, and observable data pipelines.
• Experience designing and optimizing data warehouse solutions in Snowflake or comparable cloud data platforms.
• Experience building and maintaining data transformation frameworks using dbt or similar tooling.
• Experience working with distributed data processing technologies such as Spark, Kafka, or equivalent modern data processing frameworks.
• Demonstrated ability to independently lead complex technical initiatives involving multiple stakeholders from planning through production support.
• Experience mentoring engineers, providing thoughtful technical feedback, and helping raise engineering quality across a team.
• Strong communication skills with the ability to explain technical concepts, navigate tradeoffs, and build alignment across engineering and business stakeholders.
• Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.

Interested in the role, but don’t meet every qualification? 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.

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 $150,000 - $215,000. 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.