Lead Software Engineer-AWS and Terraform

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.

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-AWS and Terraform at JPMorgan Chase within the Corporate Sector-Commercial and Investment Banking Finance 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

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Develops secure high-quality production code, and reviews and debugs code written by others
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
• Adds to team culture of diversity, opportunity, inclusion, and respect
• 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
• Hands-on experience delivering end-to-end solutions across system design, application development, testing, and operational stability for production systems
• Advanced proficiency in one or more programming languages, including Python
• Practical cloud-native engineering experience, including designing and operating workloads on AWS
• Strong AWS compute expertise (EC2, ECS/EKS, and/or Lambda), with an emphasis on performance, resiliency, and cost awareness
• Solid AWS networking knowledge (VPC, subnets, security groups, NACLs, Route 53, load balancers) to design secure and resilient architectures
• Experience with AWS storage and data platforms (S3, EBS, EFS; and databases such as RDS/Aurora/DynamoDB as applicable) to support varied application needs
• Proven Infrastructure-as-Code skills with Terraform, including authoring/maintaining reusable modules, managing remote state and locking, and applying workspace/environment best practices
• Strong automation and delivery capabilities, including CI/CD pipeline design, integrating Terraform into workflows, automated validation/testing (linting, plan approvals), and scripting (Python, Bash, or PowerShell) to streamline engineering processes
• 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

• Experience with modern front-end technologies
• Experience working in financial services