The RoleThe 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 - and 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, working closely with Software Engineers, Data Engineers, Analytic Engineers and Product. The platform runs on Python, Airflow, Kubernetes, configuration YAML, Great Expectations, and Terraform.
What You Will Do- 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.
What You Will Need to Succeed- 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.
- 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.
- Data governance & observability: experience with data governance frameworks, data catalogs, and observability tooling that keep large-scale data assets discoverable, trusted, and compliant.
- Technical leadership: experience setting technical direction for a platform or team, translating ambiguous requirements into clear architecture, and mentoring other engineers.
- 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.
- 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.
- Quality mindset: experience embedding automated test coverage, data validation, and idempotent design into deployment pipelines.
- Design principles: security, trust, and dependability are foundational to how you build - declarative design, modularity, containers, and idempotency should genuinely excite you.
What You Bring to the Table- Experience: 8+ years building production data systems, with deep expertise in large-volume data pipelines, cloud databases, and real-time data events.
- Track record: demonstrated experience as a technical lead - mentoring engineers and driving architecture decisions for a team or platform.
- Coding proficiency: strong hands-on experience with Python (Python 3).
- 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.
- Modern data stack: familiarity with configuration-driven pipeline design, Airflow, and CI/CD for data workflows.
- 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.
- AI-forward: hands-on experience using AI tools to accelerate how you work.
- Team mindset: you take pride in what your team accomplishes, not just your individual output, and communicate with clarity and openness.
What We OfferWe have all the usual perks and benefits but what we can really offer you is a fantastic work environment powered by an amazing team.
- Industry competitive compensation and equity plan
- Flexible time off, sick days, and 13 paid holidays
- Comprehensive medical insurance including Health, Dental and Vision
- Paid parental leave (plus fertility, adoption and other family planning benefits)
- In-office workspace (San Francisco & Chicago)
- Monthly allowance for wellness, reading and access to LinkedIn Learning for continued development
- Events and activities both team-based and company wide that inspire, educate and cultivate
The US base salary range for this position in the United States is $166,700 - $232,000 USD per year. Our salary ranges are determined by role, level, and location. At Pantheon, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.
