Senior Data Platform Engineer — Scale Lakehouse and AI

Femtech Insider Ltd.

Boston (MA)

On-site

USD 150,000 - 215,000

Full time

8 days ago

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Job summary

WHOOP is seeking a Senior Software Engineer, Data Platform to build and evolve the foundational infrastructure powering our data platform, lakehouse, and real-time systems. You will partner with data engineers, ML platform engineers, software engineers, and data scientists to design, develop, and operate core services enabling scalable data movement.

You are an owner who drives architecture, reliability, and operational excellence, delivering production-grade systems while leveraging AI-assisted

Qualifications

BS in Computer Science, Engineering, or equivalent experience.
5+ years of experience in data engineering, platform engineering, or software engineering with 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.

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