About the Staff Data Engineer at Headspace:
The Staff Data Engineer at Headspace will play a foundational role in building the data platform that powers personalized, ethical, and scalable mental health support. This person will lead the evolution of our privacy-first data infrastructure, enabling better experimentation, AI-ready capabilities, and smarter decision-making across the company. It’s a high-impact staff-level role for someone who wants to shape technical strategy, mentor others, and help drive how Headspace delivers more personalized care to millions of members.
What you will do:
- Lead the implementation of a resilient, privacy-first data platform architecture that powers personalization, ethical AI, and accelerates data-driven decisions
- Lead the design, infrastructure, and tooling decisions for platform optimization and consolidation efforts, including the deprecation of legacy and redundant tooling such as Appflow, Prefect, Stitch, Redshift, and Looker
- Develop AI-ready architecture by creating semantic layers that define and standardize business logic, enabling data for Agentic AI, and spearheading new support for ML, experimentation, and onboarding to experimentation pipelines like Statsig
- Mentor other members of the DE and broader data team, particularly around dbt architecture and query performance., Foster a data-first culture that prioritizes excellence, innovation, and collaboration across teams
- Act as a technical thought leader, shaping the company’s data strategy and influencing cross-functional roadmaps with data-centric solutions
What you will bring:
Required Skills:
- 6+ years in data engineering, with extensive experience in data platform development, complex data model designing, and data compliance implementation
- Production experience writing performant Python and PySpark code on distributed compute (Spark 3+, Delta Lake)
- Existing experience with Databricks is preferred or experience with Redshift and/or Snowflake
- Demonstrated expertise in architectural patterns for building high-volume ETL pipelines
- Experience with data modeling, Medallion architecture, pipeline design, metrics calculation and technical documentation
- Exceptional oral and written communication abilities, facilitating effective cross-functional collaboration
- BA/BS degree in Computer Science, Engineering, or a related field, or equivalent practical experience
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