Senior Data Product Engineer

At WHOOP, were 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 Products Engineer to build trusted, reusable data products that power decision-making across the company. Sitting at the intersection of software engineering, analytics, and product thinking, you will transform raw data into well-modeled, discoverable, and governed data products that accelerate experimentation, machine learning, operational reporting, and strategic decision-making. You will partner closely with Product, Analytics, Data Science, Engineering, and business stakeholders to ensure WHOOP has a consistent, trusted foundation for understanding its data. As a senior member of the team, youll help establish modern data product practices while mentoring others and raising the bar for engineering quality across the organization.

Responsibilities

• Design, build, and own business-critical data products, transforming raw operational data into trusted, reusable datasets that enable analytics, experimentation, and machine learning.

• Develop scalable dimensional models, semantic layers, and dbt transformations that create consistent business logic and trusted metrics across WHOOP.

• Partner closely with Product, Analytics, Data Science, and Engineering teams to understand business needs, translate ambiguous requirements into well-designed data products, and ensure those products evolve alongside the business.

• Establish engineering best practices for data modeling, testing, documentation, lineage, and governance, improving trust, discoverability, and maintainability across the analytical ecosystem.

• Collaborate with Data Engineers and Data Platform Engineers to improve upstream data quality, influence data contracts, and ensure reliable delivery of business-critical datasets.

• Drive adoption of reusable data products and self-service analytics capabilities, reducing duplication of business logic and enabling teams to move faster with confidence.

• Mentor engineers and analysts on modern data modeling techniques, engineering best practices, and data product thinking through technical leadership, design reviews, and collaborative problem solving.

• Leverage AI tools to accelerate development, improve documentation, enhance data quality, and increase engineering productivity while maintaining rigorous validation, governance, and quality standards.

Qualifications

• Bachelors degree in Computer Science, Engineering, Information Systems, Analytics, or a related technical field, or equivalent practical experience.

• 5+ years of experience designing and building production data solutions with a strong emphasis on analytics engineering, data modeling, or data product development.

• Expert-level SQL skills and extensive experience designing dimensional models and analytical data structures that balance usability, performance, and maintainability.

• Professional experience building data transformation frameworks using dbt or similar modern data transformation tools.

• Strong understanding of semantic modeling, metric design, and data governance principles, with experience creating trusted business-facing datasets.

• Experience working with modern cloud data warehouses such as Snowflake and partnering with data engineering teams to build scalable analytical solutions.

• Demonstrated ability to lead complex cross-functional initiatives, balancing technical excellence with business outcomes and stakeholder needs.

• Experience mentoring engineers and influencing technical standards through design reviews, documentation, and collaborative leadership.

• Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences and build alignment across diverse stakeholder groups.

• Passion for treating data as a product, with a strong focus on usability, quality, discoverability, and long-term maintainability.

• 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.

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 eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

The 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.