Senior Data Product Engineer

Whoop•Boston, MA

12h•Onsite

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. 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, you'll help establish modern data product practices while mentoring others and raising the bar for engineering quality across the organization.

Requirements

Bachelor's 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.

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.