Lead Platform Engineer DevOps / AWS / Kubernetes

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Platform Engineering team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Leads the design, development, and evolution of a highly scalable and reliable GraphQL platform serving multiple teams and business units

• Utilizes containerization and orchestration technologies such as Docker and Kubernetes to manage large-scale workloads

• Establishes and champions observability, monitoring, and alerting standards across the platform, designing proactive solutions to detect and resolve issues before they impact users

• Leads the development of automation strategies for CI/CD pipelines and infrastructure-as-code practices, creating reusable patterns and frameworks that accelerate delivery across engineering teams

• Designs and oversees intuitive self-service developer experiences, including APIs, tooling, documentation, and integration patterns that enable teams to adopt platform services independently

• Contributes to open-source projects or technical communities related to GraphQL, platform engineering and AWS services

• Scripts and automates using Python and utilizes Terraform and infrastructure-as-code practices for managing complex, multi-environment infrastructure

• Architects in GraphQL architecture, schema design, and RESTful API, with experience designing and implementing API standards

• Utilizes workflows (Git/Bitbucket) and distributed systems monitoring using tools such as Splunk, DataDog, Dynatrace, or CloudWatch

• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience

• Proven leadership experience in mentoring engineers, leading technical initiatives, and driving architectural decisions across teams

• Expert in containerization and orchestration technologies such as Docker and Kubernetes and expert-level proficiency in scripting and automation using Python or similar language (Bash, Groovy)

• Deep hands-on experience with Terraform and infrastructure-as-code practices for managing complex, multi-environment infrastructure

• Strong expertise in GraphQL architecture, schema design, and RESTful API principles, with experience designing and implementing API standards

• Expert-level proficiency with version control workflows (Git/Bitbucket) and distributed systems monitoring using tools such as Splunk, DataDog, Dynatrace, or CloudWatch

• Deep understanding of OAuth 2.0, secure authentication/authorization patterns, and security best practices in platform engineering

• Extensive experience with AWS cloud architecture and services, including architectural patterns for high availability and disaster recovery

• Exceptional documentation skills, including creating comprehensive technical documentation, architecture decision records (ADRs), runbooks, and system diagrams

• Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices

Preferred qualifications, capabilities, and skills

• Advanced knowledge of AWS services including EKS, ECS Fargate, IAM, VPC design, CloudWatch, X-Ray, ElastiCache-Redis, RDS Aurora Postgres, MSK, and KMS

• Proficient in multiple languages (Java, Rust, Go, or similar) with the ability to review code and provide technical guidance

• Proven track record designing and implementing comprehensive observability solutions (metrics, logging, tracing, alerting) and automating complex CI/CD workflows using Jenkins, Spinnaker, or similar platforms

• Experience with open-source projects or technical communities related to GraphQL, platform engineering, or cloud-native architectures

• Experience with AIOps, including deploying monitoring/automation agents to improve observability, reduce alert noise, and increase alert precision