Senior Platform Reliability Engineer – Data & Observability

Remote; San Francisco (CA); On-site; USD 200,000 - 270,000; Full time

Job summary

Databricks in San Francisco is seeking a Sr. Staff Platform/Data Reliability Engineer to own the operational excellence of the Databricks platform, ensuring reliable, secure, and scalable enterprise use.

You will define CI/CD standards, promote environments, and establish platform governance across data jobs, notebooks, and infrastructure. You will collaborate with data engineers, cloud/infrastructure teams, and security to enforce controls, observability, and cost-aware production patterns.

Qualifications

• 12+ years of relevant experience in data platform engineering, platform operations, site reliability engineering, or modern cloud data infrastructure.
• Hands-on experience with Databricks or a closely related cloud data platform in production environments.
• Experience designing or operating CI/CD, environment promotion, version control, and deployment automation for data platforms and pipelines.
• Strong understanding of platform operations concepts such as observability, monitoring, alerting, incident management, and reliability engineering.
• Experience with compute policy design, workload isolation, service principals, and secure production execution patterns on cloud data platforms.
• Ability to work effectively in a regulated or security-sensitive environment with strong expectations around access control, auditability, and operational discipline.
• Strong collaboration skills and comfort partnering with cloud/infrastructure, security, data engineering, and analytics stakeholders.

Responsibilities

• Own operational excellence for the Databricks platform, including monitoring, alerting, observability, incident response support, and production runbook patterns for data jobs and platform services.
• Define and maintain CI/CD and promotion standards for Databricks assets, including workflows, jobs, notebooks, code packages, infrastructure configuration, and environment promotion from dev to prod.
• Design and maintain platform standards for job orchestration, cluster and compute policies, service principal usage, environment isolation, and production execution reliability.
• Establish reusable operational templates and enablement patterns for new domains onboarding to Databricks, including logging conventions, job tagging, metadata capture, and support handoff expectations.
• Partner with the Senior Data Engineer to ensure ingestion and medallion patterns are implemented in a way that is observable, recoverable, cost-aware, and secure in production.
• Work with the cloud/infrastructure team to align Databricks configuration and usage patterns with broader enterprise cloud standards, especially where commercial and future government-hosted environments are involved.
• Help enforce technical controls for data segregation, access boundaries, and operational compliance in a highly regulated environment.
• Track and improve platform health metrics such as job success rates, incident trends, data pipeline reliability, cost efficiency, and environment drift.
• Document platform standards, operational expectations, and support models so the Databricks platform can scale beyond a small founding team.
• Mentor internal engineers who are growing into platform responsibilities, helping expand Databricks operational knowledge within the team.

Skills

Platform design

Observability

Reliability engineering

Security awareness

Tools

Databricks

Delta Lake

Unity Catalog

CI/CD

IaC

Job description

Databricks in San Francisco is seeking a Sr. Staff Platform/Data Reliability Engineer to own the operational excellence of the Databricks platform, ensuring reliable, secure, and scalable enterprise use.

You will define CI/CD standards, promote environments, and establish platform governance across data jobs, notebooks, and infrastructure. You will collaborate with data engineers, cloud/infrastructure teams, and security to enforce controls, observability, and cost-aware production patterns.