Lead Site Reliability Engineer

Job responsibilities

• Demonstrates and champions site reliability culture and practices and exerts technical influence throughout your team
• Leads initiatives to improve the reliability and stability of your team's applications and platforms using data-driven analytics to improve service levels
• Collaborates with team members to identify comprehensive service level indicators and stakeholders to establish reasonable service level objectives and error budgets with customers
• Uses enterprise-authorized AI capabilities within the work environment to accelerate major-incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements.
• Demonstrates a high level of technical expertise within one or more technical domains and proactively identifies and solves technology-related bottlenecks in your areas of expertise
• Acts as the main point of contact during major incidents for your application and demonstrates the skills to identify and solve issues quickly to avoid financial losses
• Documents and shares knowledge within your organization via internal forums and communities of practice
• Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., CI/CD quality checks, test/validation automation, and operational readiness), ensuring traceability/auditability, resiliency, and security controls.

Required qualifications, capabilities, and skills

• Formal training or certification on site reliability engineering concepts and 5+ years applied experience
• Deep proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices with the ability to implement these practices within an application or platform
• Fluency in at least one programming language such as (e.g., Python, Java Spring Boot, .Net, etc.)
• Deep knowledge of software applications and technical processes with emerging depth in one or more technical disciplines
• Proficiency and experience in observability such as white and black box monitoring, SLO alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve SRE workflows (e.g., incident investigation support and knowledge capture) with strong validation habits and awareness of data sensitivity.
• Ability to evaluate AI-assisted operational recommendations for correctness and risk, define appropriate guardrails for team usage, and ensure outcomes align to resiliency and security expectations.
• Proficiency in continuous integration and continuous delivery tools (e.g., Jenkins, GitLab, Terraform, etc.)
• Experience with container and container orchestration (e.g., ECS, Kubernetes, Docker, etc.)

• Experience with troubleshooting common networking technologies and issues