Senior Data Engineer
Job description
About the role
You will lead the design, development, and long-term ownership of the data systems that power analytics, experimentation, machine learning, and business decision-making across WHOOP. In this role, you will partner closely with Data Science, Analytics, Product, and Engineering teams to translate ambiguous business requirements into maintainable technical solutions. You will raise the technical bar through mentorship, thoughtful engineering practices, and a commitment to continuously improving how Data Engineering operates. The position requires you to own complex cross-functional data initiatives from design through production while proactively identifying risks, dependencies, and tradeoffs. You will leverage AI tools and automation to accelerate development, improve engineering quality, and increase team productivity. The role emphasizes driving improvements in data quality, observability, testing, documentation, and operational excellence. You will also contribute to the technical direction of the Data Engineering organization by evaluating new technologies and improving engineering standards.
Key facts
What you'll do
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.
Champion data reliability and consistency by implementing robust testing strategies, monitoring, and alerting to support high-trust decision-making across the company.
Collaborate with Data Scientists to ensure analytical datasets, features, and experiments are delivered with the performance, stability, and governance required for advanced modeling work.
Work closely with Product and Analytics stakeholders to define data requirements, clarify specifications, and ensure that metrics are well-defined, repeatable, and trusted.
Optimize data infrastructure for cost, performance, and scalability, ensuring that solutions meet current demands while being extensible for future growth.
Establish and maintain strong data governance, documentation, and lineage practices to enable self-serve analytics and reduce operational overhead.
Participate in on-call rotations and incident response activities to address data issues, investigate root causes, and implement preventative measures.
Requirements
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.