Staff Software Engineer (Data Platform)

Join Pantheon as a Staff Software Engineer (Data Platform) and take our robust, configuration-driven data platform to the next level. You will serve as the lead technical member of the Data Platform team, enhancing platform architecture, building with modern data infrastructure, mentoring the team, and owning the full lifecycle of development, testing, operations, and support. You will also partner cross-functionally with various teams to deliver high-impact data solutions and support a self-service, data-driven culture across Pantheon.

• Extend and enhance the data platform to surface insights and power machine learning infrastructure.
• Serve as the lead technical member of the Data Platform team, maintaining and enhancing core platform services.
• Mentor a team of engineers in designing and implementing high-impact projects, raising the technical bar across the group.

• Data governance & observability: experience with data governance frameworks, data catalogs, and observability tooling that keep large-scale data assets discoverable, trusted, and compliant
• Coding proficiency: strong hands-on experience with Python (Python 3)
• Cloud & infrastructure: experience with Google Cloud Platform (preferred) or a comparable cloud environment, plus containers, Kubernetes, and Terraform
• Communication: the ability to clearly articulate technical designs, project status, and risk to both technical peers and non-technical stakeholders, while remaining open to others’ ideas
• Experience: 8+ years building production data systems, with deep expertise in large-volume data pipelines, cloud databases, and real-time data events
• Distributed data systems: deep understanding of processing large-scale datasets across distributed systems, with a clear grasp of the trade-offs in designing for high throughput and low latency
• Team mindset: you take pride in what your team accomplishes, not just your individual output, and communicate with clarity and openness
• Database knowledge: hands-on experience with cloud data warehouses such as Snowflake, BigQuery, Firebolt, or Redshift
• ML/LLM infrastructure: experience building or supporting the data infrastructure behind Machine Learning and LLM applications — feature stores, embedding/vector pipelines, or model-ready data services
• Modern data stack: familiarity with configuration-driven pipeline design, Airflow, and CI/CD for data workflows
• Quality mindset: experience embedding automated test coverage, data validation, and idempotent design into deployment pipelines
• Technical leadership: experience setting technical direction for a platform or team, translating ambiguous requirements into clear architecture, and mentoring other engineers
• Track record: demonstrated experience as a technical lead — mentoring engineers and driving architecture decisions for a team or platform
• AI-forward: hands-on experience using AI tools to accelerate how you work
• Design principles: security, trust, and dependability are foundational to how you build — declarative design, modularity, containers, and idempotency should genuinely excite you
• Data modeling and architecture: ability to design, implement, and optimize scalable data models (dimensional, normalized) for both OLAP and OLTP systems, ensuring data integrity and query performance
• Customer/product focus: an understanding of the direct and indirect business value of your work, ensuring data solutions align with company-wide goals and deliver impact for internal and external customers