Job responsibilities

• Create and deliver high-quality designs, roadmaps, and program charters in partnership with engineering teams
• Act as a key mentor and trusted advisor to technologists on technical and business challenges, serving as a culture carrier and site reliability adoption champion
• Collaborate to design and implement observability and reliability capabilities for complex systems that are robust, stable, and reduce operational toil and technical debt
• Use enterprise-authorized AI capabilities within the work environment to accelerate reliability design and operational decision-making (e.g., incident and post-incident analysis, requirements traceability), validating outputs and handling operational data according to sensitivity and security requirements
• Drive the evolution and debugging of critical components by understanding application and platform dependencies, constraints, and failure modes
• Provide ongoing guidance, tools, and solutions that enable safe growth through stronger reliability, scalability, and operational readiness
• Contribute to the site reliability engineering community through knowledge sharing, reusable patterns, and continuous improvement practices
• Lead reuse-first adoption of AI-assisted reliability workflows across software development life cycle practices (e.g., testing and validation automation, production readiness), ensuring traceability, resiliency, and security controls
• Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes, while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the software development life cycle/toolchain
• Apply knowledge of tools within the software development life cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale

Required qualifications, capabilities and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Experience defining and operationalizing non-functional requirements, availability targets, and production readiness standards for customer-facing services
• Experience establishing and managing service level indicators and service level objectives with stakeholders, including measurement, alerting, and continuous improvement
• Proficiency in Python and/or Java for building automation, tooling, and reliability improvements
• Knowledge of identity and access management concepts and secure access patterns in enterprise environments
• Experience with Amazon Web Services, including database platforms and data movement patterns such as extract, transform, load processes
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls

Preferred qualifications, capabilities and skills

• 10+ years developing, supporting, or leading reliability efforts for enterprise-scale applications and platforms
• Experience designing observability strategies (metrics, logs, traces) and operational dashboards tied directly to customer experience outcomes
• Experience reducing operational toil through self-service tooling, automation, and reliability-first engineering practices
• Experience influencing cross-functional stakeholders (product, engineering, operations, risk) through clear reliability narratives and measurable outcomes
• Experience leading large-scale modernization, resiliency upgrades, or reliability transformations across multiple teams