JPMorgan Chase
Lead Platform Engineer DevOps / AWS / Kubernetes
1111 Polaris Pkwy, Columbus, OH 43240
Full-time
First seen by offpeak October 21, 2026
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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.
- 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
- 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