winterchill jobs
← all jobs
Direct

Lead Site Reliability Engineer

JPMorgan· LONDON, United Kingdom· £73,800–£99,600/yr (est.)Licensed sponsor
Posted 30 Jul 2026 · Added 30 Jul 2026, 11:22
JPMorgan3.9 (20,400)10,000+ employees

Headquartered in New York City, JPMorgan Chase is the largest bank in the United States.

Levels.fyi · global compSWE $164k TC ($122k–$205k)
AI summary

Works with AWS, Python, Grafana, Dynatrace, Prometheus, Datadog, Splunk, Jenkins, GitLab, Terraform, ECS, Kubernetes, and Docker. As a lead on the Infrastructure Platforms team, you improve reliability and stability of applications/platforms, conduct resiliency design reviews, and mentor engineers.

View original on Direct
See how well this job fits your CV.

Free Tailor for ATS: 10/10 runs left

Assume a critical role in defining the future of a globally recognized firm and have a direct and significant effect in a realm tailored for top achievers in site reliability.

As a Lead Site Reliability Engineer at JPMorgan Chase within the Infrastructure Platforms team, you hold a leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. Take lead and conduct resiliency design reviews, break up complex problems into digestible work for other engineers, act as a technical lead for medium to large-sized products, and provide advice and mentoring to other engineers.

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

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

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.

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 advanced applied experience

Should be able to Design and code complex problems in Public cloud like AWS.

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 Python & 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.

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

Ability to identify and solve problems related to complex data structures and algorithms

Drive to self-educate and evaluate new technology, ability to teach new programming languages to team members and ability to expand and collaborate across different levels and stakeholder groups

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.