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