Staff Software Engineer - Data Platform

Pantheon’s Data Platform team builds and operates data infrastructure and analytics across the company. The Staff Software Engineer will lead the technical evolution of the platform, bringing data insights into product experiences and supporting machine-learning and LLM-driven capabilities.

Responsibilities:

• Extend our robust, configuration-driven data platform to surface data insights directly within the Product experience, and to power the infrastructure behind Machine Learning and LLM-driven insights
• Serve as the lead technical member of the Data Platform team, maintaining and enhancing our core platform services: Ingest as a Service (consistent extraction and loading into the data lake), Curation as a Service (configuration-driven, audited transformations) and Retention as a Service (historical data hosting that balances access, compliance, and cost)
• Work hands-on with Snowflake (including Snowpark for Python), Google Cloud Platform, Airflow , Docker, and Terraform
• Guide a team of engineers in designing and implementing high impact projects, raising the technical bar across the group
• Operate in a full DevOps model — development, testing, operations, and support for the systems you build
• Drive continuous improvement of engineering standards for coding, testing, deployment, and communication
• Work with Product, Sales, Ops, Finance and other teams to deliver high-impact data solutions and support a self-service, data-driven culture across Pantheon
• Participate in the Data team’s on-call rotation, contributing to the stability, reliability, and performance of Pantheon’s data infrastructure

Requirements:

• 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
• ability to design, implement, and optimize scalable data models (dimensional, normalized) for both OLAP and OLTP systems, ensuring data integrity and query performance
• experience with data governance frameworks, data catalogs, and observability tooling that keep large-scale data assets discoverable, trusted, and compliant
• experience setting technical direction for a platform or team, translating ambiguous requirements into clear architecture, and mentoring other engineers
• 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
• 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 embedding automated test coverage, data validation, and idempotent design into deployment pipelines
• security, trust, and dependability are foundational to how you build — declarative design, modularity, containers, and idempotency should genuinely excite you
• 8+ years building production data systems, with deep expertise in large-volume data pipelines, cloud databases, and real-time data events
• demonstrated experience as a technical lead — mentoring engineers and driving architecture decisions for a team or platform
• strong hands-on experience with Python (Python 3)
• hands-on experience with cloud data warehouses such as Snowflake, BigQuery, Firebolt, or Redshift
• experience with Google Cloud Platform (preferred) or a comparable cloud environment, plus containers, Kubernetes, and Terraform
• familiarity with configuration-driven pipeline design, Airflow, and CI/CD for data workflows
• experience building or supporting the data infrastructure behind Machine Learning and LLM applications — feature stores, embedding/vector pipelines, or model-ready data services
• hands-on experience using AI tools to accelerate how you work
• communicate with clarity and openness