As a Senior Lead Site Reliability Engineer at JPMorganChase within the Core Engineering Solutions team of Consumer and Community Banking, you are an integral part of a team that works to develop high-quality architecture solutions for various software applications and platform products. You collaborate with stakeholders to define non-functional requirements and availability targets, ensuring these are embedded in product design and testing. You drive significant business impact by championing site reliability principles, implementing robust observability, and shaping the target state architecture through your expertise. You will work alongside talented engineers dedicated to delivering resilient, scalable, and innovative solutions that support millions of customers.

Job responsibilities

• Creates high quality designs, roadmaps, and program charters that are delivered by you or the engineers under your guidance.
• Provides advice and mentoring to other engineers and acts as a key resource for technologists seeking advice on technical and business-related issues.
• Demonstrates site reliability principles and practices every day and champions the adoption of site reliability throughout your team.
• Collaborates with others to create and implement observability and reliability designs for complex systems that are robust, stable, and do not incur additional toil or technical debt.
• Works toward becoming an expert on the applications and platforms in your remit while understanding their interdependencies and limitations.
• Evolves and debugs critical components of applications and platforms.
• Provides comprehensive and ongoing guidance, tools, and solutions to support the firm’s growth.
• Makes significant contributions to JPMorganChase’s site reliability community via internal forums, communities of practice, guilds, and conferences.
• Uses enterprise-authorized AI capabilities within the work environment to accelerate reliability design and operational decisioning (e.g., incident/post-incident analysis and requirements traceability), validating outputs and handling operational data according to sensitivity and security requirements.
• Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., testing/validation automation and production readiness), ensuring traceability/auditability, resiliency, and security controls.
• Sets team practices for safe AI usage in operations, including review/approval expectations and escalation paths, while maintaining resiliency, security, and auditability outcomes.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Advanced knowledge in site reliability culture and principles with demonstrated ability to implement site reliability within an application or platform.
• Hands-on deep expertise in public cloud and modernization journey.
• Advanced knowledge and experience in observability such as white and black box monitoring, service level objectives, 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 reliability engineering workflows with strong validation habits and awareness of data sensitivity.
• Advanced knowledge of software applications and technical processes with considerable depth in one or more technical disciplines.
• Experience working with incident management and troubleshooting defects found during testing.

Preferred qualifications, capabilities, and skills

• Ability to communicate data-based solutions with complex reporting and visualization methods.
• Recognized as an active contributor of the engineering community.
• Continues to expand network and leads evaluation sessions with vendors to see how offerings can fit into the firm’s strategy.
• Strong communication skills with ability to mentor and educate others on site reliability principles and practices.
• Experience setting team practices for safe AI usage in operations, including review/approval expectations and escalation paths.
• Familiarity with AI-assisted reliability workflows and operational decisioning.
• Experience ensuring traceability, auditability, resiliency, and security controls in AI-enabled environments.