Staff Data Engineer

A venture-backed startup in San Francisco is scaling rapidly and investing heavily in the data infrastructure that powers its core business operations. With strong growth momentum and an ambitious technical roadmap, they're looking for a senior data engineering leader to own and evolve the platform that the entire organization runs on.

In this role, you'll architect and scale a production-grade data platform — spanning orchestration, infrastructure, data modeling, and governance — while serving as a technical anchor for cross-functional partners and a mentor to a growing engineering team.

Staff Data Engineer

Location: San Francisco (Hybrid) Type: Full-time

Salary: $200–230k (DOE) + Equity + Top Tier Benefits

Responsibilities

• Architect and scale a high-availability data platform serving teams across the organization including Product, Engineering, Data Science, Marketing, and Finance
• Establish and steward enterprise-wide data models across core business domains, creating a unified and consistent data layer.
• Own end-to-end pipeline orchestration using tools like Airflow in cloud-native environments
• Continuously evaluate and improve internal tooling and infrastructure to reduce operational burden and accelerate team output
• Partner with engineering leadership to shape the long-term data roadmap and drive prioritization
• Coach and develop junior engineers, raising the technical bar across the team
• Define and uphold data governance standards and security controls across the platform

What We're Looking For

• 7+ years in a data engineering capacity with a strong foundation in software development principles
• Demonstrated experience architecting ETL systems and reusable framework components
• Fluency with pipeline orchestration platforms such as Airflow, Prefect, or Dagster
• Practical experience deploying and managing workloads across AWS, GCP, or Azure
• Comfort with modern infrastructure tooling including Terraform and Kubernetes
• Clear, confident communicator across both technical and business audiences
• Experience with Spark or comparable distributed processing frameworks a plus