Staff Software Engineer (Data Platform)

Join Pantheon as a Staff Software Engineer focused on enhancing their data platform. Enjoy competitive pay and flexible work arrangements.

• 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- Track record: demonstrated experience as a technical lead — mentoring engineers and driving architecture decisions for a team or platform
• Team mindset: you take pride in what your team accomplishes, not just your individual output, and communicate with clarity and openness
• 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
• Technical leadership: experience setting technical direction for a platform or team, translating ambiguous requirements into clear architecture, and mentoring other engineers
• Design principles: security, trust, and dependability are foundational to how you build — declarative design, modularity, containers, and idempotency should genuinely excite you
• AI-forward: hands-on experience using AI tools to accelerate how you work
• Experience: 8+ years building production data systems, with deep expertise in large-volume data pipelines, cloud databases, and real-time data events
• 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
• 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
• Quality mindset: experience embedding automated test coverage, data validation, and idempotent design into deployment pipelines
• Coding proficiency: strong hands-on experience with Python (Python 3)
• Data governance & observability: experience with data governance frameworks, data catalogs, and observability tooling that keep large-scale data assets discoverable, trusted, and compliant
• Database knowledge: hands-on experience with cloud data warehouses such as Snowflake, BigQuery, Firebolt, or Redshift
• 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
• Cloud & infrastructure: experience with Google Cloud Platform (preferred) or a comparable cloud environment, plus containers, Kubernetes, and Terraform
• Modern data stack: familiarity with configuration-driven pipeline design, Airflow, and CI/CD for data workflows
• 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