Lead Software Engineer – Cloud AWS Resiliency

JPMorganChase•Plano, TX

About The Position

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 and Community Banking technology - Deposits Platform, 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.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience.
Hands-on practical experience delivering system design, application development, testing, and operational stability
Hands-on experience with AWS cloud architectures, specifically DR-enabling services like AWS Elastic Disaster Recovery, AWS Backup, and multi-AZ/multi-region deployments.
Proficiency in at least one modern language (Python, Go, or Bash) and familiarity with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
5+ years in Site Reliability Engineering (SRE), Disaster Recovery Planning, or Distributed Systems Engineering.
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 understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

Nice To Haves

In-depth knowledge of the financial services industry and their IT systems
Practical cloud native experience

Responsibilities

Lead and coordinate end-to-end DR tests and real-time failover events across all applications within the product portfolio, ensuring smooth cross-team collaboration.
Develop and maintain overarching resiliency frameworks that development and infrastructure teams can consume via automated product offerings and repeatable patterns.
Define and monitor Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO) for all product components, tracking metrics to identify architectural or procedural gaps.
Partner with application teams to automate disaster recovery provisioning, scaling, configuration, and monitoring using Infrastructure as Code (IaC) tools like Terraform.
Drive resiliency testing scenarios and chaos engineering using native tools like AWS Fault Injection Service (FIS) and AWS Resilience Hub to identify vulnerabilities before they impact production
Partner with SREs to build advanced detection capabilities, leveraging ex-AWS Resilience Hub to assess application resilience and set up proactive CloudWatch alerts.
Establish, maintain, and execute technical DR playbooks and recovery runbooks, ensuring sequence mapping and dependencies are meticulously documented.
Mentor and lead software engineers across the organization, promoting continuous learning and operational best practices.
Collect, govern, and report on DR test artifacts, audit trails, and resilience maturity scores to demonstrate compliance with internal and regulatory standards.
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.