Lead Platform Engineer DevOps / AWS / Kubernetes

JPMorgan Chase & Co.; Columbus (OH); On-site; USD 180,000 - 230,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Lead Software Engineer to advance a scalable GraphQL platform within the Consumer & Community Banking Platform Engineering team.

You will drive architectural decisions, mentor engineers, and implement containerization, IaC, and AI-assisted practices across multiple business units while maintaining secure, observable, and reliable systems.

Qualifications

• Formal training or certification in 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 IaC 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 for high availability and DR.

Responsibilities

• Leads the design, development, and evolution of a scalable GraphQL platform for multiple teams.
• Uses Docker and Kubernetes to manage large-scale workloads.
• Establishes observability, monitoring, and alerting standards across the platform.
• Drives automation strategies for CI/CD pipelines and IaC practices.
• Designs self-service developer experiences, APIs, tooling, and documentation.
• Contributes to open-source projects related to GraphQL and cloud-native platforms.
• Scripts and automates using Python; uses Terraform for multi-environment infrastructure.
• Architects GraphQL, RESTful APIs, and API standards; oversees security-conscious designs.
• Utilizes Git/Bitbucket workflows and monitoring tools like Splunk, DataDog, Dynatrace, CloudWatch.
• Promotes AI-assisted engineering practices with validated outputs and secure coding standards.

Skills

GraphQL architecture

AWS cloud

Docker & Kubernetes

Terraform

Python scripting

CI/CD automation

Security best practices

Git/Bitbucket

Observability tooling

AI-assisted engineering

Education

Formal software engineering training or certification

Tools

Docker

Kubernetes

Terraform

Python

Git/Bitbucket

AWS

Splunk

DataDog

Dynatrace

CloudWatch

Job description

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 knowledgeof 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 recorddesigning 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