Senior Lead Site Reliability Engineer

JPMorgan Chase & Co.•Jersey City, NJ

18h•$175,750 - $260,000

About The Position

As a Senior Lead Site Reliability Engineer at JPMorgan Chase within the Commercial Investment Banking team of Fraud Prevention, you will solve complex and broad business problems with simple and straightforward solutions. Through code and cloud infrastructure, you will configure, maintain, monitor, and optimize applications and their associated infrastructure to independently decompose and iteratively improve on existing solutions. You are a significant contributor to your team by sharing your knowledge of end-to-end operations, availability, reliability, and scalability of your application or platform. You are an integral part of a team that works to develop high-quality architecture solutions for various software applications and platform products. You drive significant business impact and help shape the target state architecture through your capabilities in multiple architecture domains. You will ensure the platform is reliable, secure, performant, and resilient in production across Kubernetes-based environments and AWS. You will apply SRE principles to drive measurable improvements in availability and latency, reduce operational toil through automation, and strengthen deployment safety and recovery capabilities in close partnership with engineering and platform teams.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Experience in SRE/DevOps/production engineering or equivalent
Hands-on experience operating Kubernetes workloads (deployments, scaling, debugging)
Practical experience with AWS (EKS, ECS, Lambda, Dynamo DB, S3) in production
Experience with CI/CD and release tooling such as Spinnaker and/or Harness
Proficiency with Terraform (IaC), and scripting/automation (Python/Bash/Go)
Strong incident response skills, RCA writing, and ability to drive remediation work
Solid fundamentals in Linux, networking, and troubleshooting distributed systems
Ability to independently execute well-scoped reliability work and escalate when needed
Working knowledge of using enterprise-authorized AI capabilities within the work environment to support SRE workflows with strong validation habits and awareness of data sensitivity
Ability to validate AI-assisted operational recommendations before applying changes, escalating when uncertain and following data sensitivity requirements

Nice To Haves

Experience implementing SLO programs and alerting aligned to customer journeys
Experience with performance testing, capacity planning, and resilience testing (fault injection/chaos, DR exercises)
Experience improving operational maturity: standardized runbooks, automated health checks, auto-remediation, and deployment guardrails
Experience with fraud screening/decisioning or payment flows
Familiarity with database reliability patterns (capacity, backups, failover readiness)
Experience with secure operational practices (least privilege, secrets handling)
Experience partnering with engineering and platform teams to drive reliability improvements

Responsibilities

Own production reliability outcomes by managing day-to-day operational health (availability, latency, throughput, error rates), proactively surfacing risks, and driving remediation.
Define and evolve service level indicators/service level objectives (SLIs/SLOs) and error budgets; build actionable, customer-impact-aligned alerting and reduce noise through tuning and standardization.
Improve end-to-end observability and troubleshooting (metrics, logs, traces), dashboards, and runbooks across Kubernetes and Amazon Web Services (AWS); perform deep technical triage of distributed-system issues.
Lead incident response and problem management by participating in on-call, driving triage/mitigation/recovery, completing root cause analyses (RCAs), and ensuring corrective and preventive actions close.
Operate Kubernetes workloads including autoscaling, rollout/rollback procedures, resource tuning, and resilience patterns for containerized services.
Operate AWS container and serverless components (for example, Amazon Elastic Kubernetes Service/Elastic Container Service/AWS Lambda) with a focus on scaling, retries, and safe failure modes.
Improve release engineering and delivery reliability by increasing the safety and repeatability of deployments using Spinnaker and Harness.
Build infrastructure as code and environment consistency by developing and maintaining Terraform modules and automation for reliable, repeatable environments.
Strengthen database and data-service reliability by partnering with engineering and platform teams to improve reliability patterns across multiple database technologies and data services (for example, DynamoDB, Amazon Simple Storage Service).
Embed security and controls into operations by applying secure operational practices and ensuring processes meet required control standards.
Lead small-to-medium initiatives end-to-end from proposal through production adoption, using enterprise-authorized AI capabilities to accelerate triage and toil reduction while validating outputs and handling operational data per sensitivity and security requirements.