Staff Site Reliability Engineer

The Opportunity:

Reporting to the Senior Director of Cloud Operations, you will build and operate reliable cloud infrastructure through hands-on software engineering, deployment automation and production troubleshooting. You will engineer and operate reliable cloud infrastructure, automate deployments and production operations, and improve performance and cost efficiency. You will use AI-assisted and agentic tools to support infrastructure design and the development, testing and maintenance of Infrastructure as Code. You will pair with application, data, scientific and platform engineers supporting research, analytics and enterprise operations. Working with Cloud Infrastructure, Security and IT, you will combine AI-enabled delivery with independent technical judgment and disciplined production controls. Key responsibilities include:

• Make production observable. Build metrics, logs, traces, dashboards and actionable alerts that expose service health and user impact. Use profiling, load testing and performance audits to identify bottlenecks across applications, containers, networks, cloud services and data platforms.

• Engineer away repetitive work. Build maintained software and AI-assisted or agentic workflows for infrastructure design, deployment, diagnostics, remediation and configuration management. Author, test and maintain IaC. Automate Kubernetes upgrades, deployments, autoscaling and workload isolation; strengthen CI/CD and GitOps validation, controlled releases and rollback. Measure and reduce operational toil.

• Lead incident recovery and disaster recovery engineering. Lead technical investigation and recovery within agreed support arrangements, followed by constructive reviews and completed corrective actions. Build reproducible recovery environments through IaC with Cloud Infrastructure; maintain runbooks; and test restoration, failover and failback against agreed recovery time and recovery point objectives.

• Improve designs across engineering teams. Pair on cloud architecture, deployment design, resilience, dependencies, retries, timeouts, resource limits and recovery behavior. Improve Kubernetes networking, access controls and troubleshooting, and Databricks and managed-platform reliability. Use AI-assisted design exploration alongside engineering analysis to optimize capacity and performance. Mentor engineers through practical reviews and pairing.

• Lead FinOps across cloud and Databricks. Own cost visibility, allocation, usage reporting, budgets, forecasting, anomaly investigation and optimization priorities. Partner with Finance on showback and commitment planning, and with Cloud Infrastructure and workload owners on measurable savings that preserve reliability, performance and security.

• Set reliability expectations. Define service-level indicators and objectives with service owners, using error budgets to inform release decisions and reliability improvements. Establish production-readiness reviews, clear service ownership and shared standards with engineering teams.

• Challenge and secure proposed changes. Critically review architectures, vendor recommendations and human- or AI-generated changes. Validate agentic workflows and outputs through testing, peer review and controlled releases. Apply least privilege, secrets management, network controls and auditability to infrastructure and agent access, retaining human accountability for production decisions.

Required Skills, Experience and Education:

• B.S. in Computer Science, Engineering or a related technical field, or equivalent practical experience; eight or more years in SRE, software engineering, platform engineering or production systems operations.

• Demonstrated production reliability improvements through service objectives, monitoring, incident management and prevention of recurring failures.

• Strong hands-on Python, Go or comparable programming, including maintained operational tools, automated tests, code review, debugging and safe failure handling.

• Strong hands-on experience with Terraform or comparable Infrastructure as Code tools, version control, automated testing and CI/CD, including building and testing disaster recovery environments.

• Production Kubernetes experience and strong Linux, networking and distributed-systems troubleshooting.

• Production experience in AWS or Azure, with the ability to work across both; strong practical cloud security experience.

• Hands-on experience using AI-assisted development or agentic workflows to design or manage infrastructure and author IaC, with evidence of testing, secure access controls, review and independent validation of generated changes.

• Experience pairing with engineering teams on deployments, system design and performance, with measurable reliability or efficiency outcomes; demonstrated cloud cost analysis and optimization experience.

• Independent technical judgment, prioritization based on service impact, risk and engineering effort, and the ability to influence cross-team decisions through clear communication, listening and collaboration.

Preferred Skills:

• EKS or AKS, Helm and GitOps deployment tools.

• OpenTelemetry, Prometheus, Grafana or comparable observability platforms.

• Advanced resilience testing and capacity modeling.

• FinOps tooling, showback, forecasting and commitment planning with Finance.

• Databricks, data pipelines, batch processing or scientific platforms.

• Self-service operational capabilities with measured toil reduction.

• Relevant advanced cloud, security or Kubernetes certifications.

#LI-YG1 #LI-Hybrid