Senior Data Platform Engineer

Responsibilities

• Design, build, and operate the core infrastructure and services that power WHOOP’s data platform, including lakehouse, warehouse, and streaming systems.
• Improve the performance, reliability, and cost efficiency of our data systems, including Iceberg, Snowflake, and AWS-based data infrastructure.
• Build and enhance internal tooling, APIs, and automation that improve platform usability and developer experience for data engineers, analysts, and data scientists.
• Lead platform engineering best practices across CI/CD, infrastructure as code, testing, deployments, and operational readiness.
• Strengthen observability and operational excellence through monitoring, alerting, incident response, and continuous improvement of production systems.
• Partner cross-functionally with Data Science, Analytics, ML Platform, and Software Engineering teams to support scalable and efficient data workflows.
• Drive best practices for data architecture, pipeline design, governance, and platform usage across the company.
• Leverage AI tools to accelerate development, improve code quality, and enhance debugging and documentation workflows.

Qualifications

• 5+ years of experience in data engineering, platform engineering, or software engineering with significant ownership of production data systems.
• Strong proficiency in Python and SQL. Java experience is nice to have.
• Experience designing and operating scalable data platforms, pipelines, or distributed data systems in production environments.
• Hands-on experience with Snowflake and modern cloud data infrastructure.
• Experience with AWS services such as S3, EKS, EMR, EC2, or related cloud-native infrastructure.
• Experience with orchestration frameworks such as Airflow or Prefect.
• Strong understanding of CI/CD, infrastructure as code, and DevOps practices for data platforms.
• Ability to independently drive technical projects and own systems end to end with a high degree of autonomy.
• Strong communication and collaboration skills, with the ability to work effectively across engineering and data teams.
• Commitment to leveraging AI-assisted development tools thoughtfully and effectively, while maintaining a high bar for engineering quality.