Senior Data Engineer, Chip

• You have built and scaled complex data products in a growing environment — you've seen platforms mature past their early ad hoc stage and know what that evolution actually takes
• Deep, hands-on knowledge of the Databricks platform end to end — workspace and compute configuration, governance, orchestration, user management, engineering best practices
• You are comfortable designing schemas and data models, including medallion architecture and Kimball/Star Schema concepts (Fact and Dimension tables), from the ground up
• You are fluent in Git workflows — branching, troubleshooting, code reviews
• You write clean, efficient code in Python, PySpark, and SQL, and understand how it performs at scale
• Experience with SQL/NoSQL databases (e.g., Redshift, Postgres, MongoDB), with a solid grounding in database fundamentals — indexing, performance, design trade-offs
• Experience in managing infrastructure as code with Terraform
• You have supported and grown less experienced engineers before, and enjoy doing it
• You design systems, not just implement them — you can reason through trade-offs and defend a decision
• You understand PII handling and governance patterns, and build them into your designs by default

Desirable

• Help bring our data platform to the next level and empower Chip to be a truly data-driven business
• Co-own the architecture of our data platform alongside the Analytics Engineer — not just implementing designs, but making the calls on how ingestion, modelling, and the semantic layer should actually work
• Provide technical leadership across the wider data engineering team as we grow
• Design, build, and run scalable, secure data pipelines across multiple sources — with an eye on compute sizing and query performance
• Co-own architecture decisions across ingestion, modelling, and the presentation layer — defining how the platform should work, not just building to spec
• Design and evolve our medallion architecture (Bronze, Silver, Gold) as the platform matures
• Define more detailed engineering standards — naming conventions, partitioning, testing strategy, contract templates
• Apply Databricks best practices and Infrastructure as Code across the platform
• Own governance patterns in Unity Catalog — permissions, access models, and how we handle sensitive data
• Set technical governance standards the rest of the team builds against
• Enable downstream consumers with self-service patterns, so the data team isn't the bottleneck for every request
• Provide technical leadership across the data engineering team — mentoring other engineers, reviewing code, and raising the engineering bar

• Video screen with someone from our Talent team
• Video interview with the hiring manager
• Final interview with VP of Engineering