Staff Software Engineer (Data Platform)

• The Data Platform team powers Pantheon’s data infrastructure and delivers analytics across the company
• We’ve built a robust, configuration-driven data platform that handles extraction, ingestion, curation, compliance, security, and enterprise-wide access to data
• We’re looking for a Staff Software Engineer to take it to the next level
• In this role, you’ll extend the platform beyond its current foundation to surface data insights directly within our Product experience, and to power the infrastructure behind Machine Learning and LLM-driven insights
• As the lead technical member of the Data Platform team, you’ll architect the next generation of services that let every team at Pantheon trust and act on data
• You’ll work closely with Software Engineers, Data Engineers, Analytic Engineers, and Product
• The platform runs on Python, Airflow, Kubernetes, configuration YAML, Great Expectations, and Terraform
• Take the platform to the next level. 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
• Enhance platform architecture. 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)
• Build with modern data infrastructure. Work hands-on with Snowflake (including Snowpark for Python), Google Cloud Platform, Airflow , Docker, and Terraform
• Mentor the team. Guide a team of engineers in designing and implementing high impact projects, raising the technical bar across the group
• Own the full lifecycle. Operate in a full DevOps model — development, testing, operations, and support for the systems you build
• Raise the bar. Drive continuous improvement of engineering standards for coding, testing, deployment, and communication
• Partner cross-functionally. 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
• Support reliability. Participate in the Data team’s on-call rotation, contributing to the stability, reliability, and performance of Pantheon’s data infrastructure

Benefits

• Health & Wellness: Taking care of you and your family is important to us. Our healthcare benefits program delivers choice and value so you can prioritize your health
• Remote & In-Office: We believe in a flexible employee experience, our San Francisco office is a center for collaboration and connection, but it’s not the only place this happens
• Flexible Time-Off: We encourage work/life balance. Take time off when you need it, and return ready to make magic on the internet when you’re ready and refreshed
• Monthly Book & Gym Allowance: One of the many ways we enable our team to take control of their development and wellness is to take advantage of our books and gym membership allowance
• Promoting Inclusivity: We strive to have a culture where Pantheors across the globe feel a high sense of belonging and engagement. We have several programs in place to help cultivate inclusion at Pantheon, including educational events, open forums, and training opportunities
• Giving Back: We believe in cultivating passion and giving back to the community we live and work in. Pantheon offers a Donation Matching program of $500 per employee and holds multiple team volunteer opportunities throughout the year
• Employee Resource Groups: Our Pantheon Resource Groups (PRGs) allow employees to connect, support each other, and spread awareness
• Professional Development: We support employee learning and development through company led-training, leadership forums, and full access to LinkedIn Learning’s catalogue of courses- Design principles: security, trust, and dependability are foundational to how you build — declarative design, modularity, containers, and idempotency should genuinely excite you
• Quality mindset: experience embedding automated test coverage, data validation, and idempotent design into deployment pipelines
• 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
• 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
• Technical leadership: experience setting technical direction for a platform or team, translating ambiguous requirements into clear architecture, and mentoring other engineers
• Data governance & observability: experience with data governance frameworks, data catalogs, and observability tooling that keep large-scale data assets discoverable, trusted, and compliant
• 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
• 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
• Experience: 8+ years building production data systems, with deep expertise in large-volume data pipelines, cloud databases, and real-time data events
• Team mindset: you take pride in what your team accomplishes, not just your individual output, and communicate with clarity and openness
• Modern data stack: familiarity with configuration-driven pipeline design, Airflow, and CI/CD for data workflows
• Coding proficiency: strong hands-on experience with Python (Python 3)
• AI-forward: hands-on experience using AI tools to accelerate how you work
• 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
• Track record: demonstrated experience as a technical lead — mentoring engineers and driving architecture decisions for a team or platform
• Database knowledge: hands-on experience with cloud data warehouses such as Snowflake, BigQuery, Firebolt, or Redshift
• Cloud & infrastructure: experience with Google Cloud Platform (preferred) or a comparable cloud environment, plus containers, Kubernetes, and Terraform