As a Lead Software Engineer at JPMorgan Chase within our agile engineering team, you will architect, design, and deliver robust, market-leading technology solutions that are secure, stable, and scalable. You will play a pivotal role in driving critical technology initiatives across multiple domains, supporting the firm’s strategic business objectives.
Your expertise will be instrumental in elevating our engineering standards and fostering innovation within the organization.
Job Responsibilities
- Lead the design, development, and implementation of scalable platform solutions, leveraging advanced troubleshooting skills and innovative approaches to address complex infrastructure and integration challenges.
- Develop and maintain high-quality, secure production code and automation scripts, ensuring seamless integration and reliability across enterprise platforms and infrastructure.
- 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.
- Produce comprehensive architecture and design documentation for complex applications, ensuring all design constraints and best practices are adhered to throughout the software development lifecycle.
- Gather, analyze, and synthesize large, diverse data sets to develop actionable visualizations and reporting, driving continuous improvement of software applications and system performance.
- Proactively uncover hidden issues and patterns within data, leveraging insights to enhance coding standards, system architecture, and overall application resiliency.
- Contribute to internal software engineering communities of practice, actively participating in events and initiatives that explore emerging technologies and industry trends.
- Champion a team culture rooted in diversity, inclusion, respect, and opportunity, mentoring junior engineers and fostering a collaborative environment.
Required Qualifications, Capabilities, and Skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- 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 proficiency in Python and/or Java, with demonstrated expertise in developing, debugging, and maintaining code using modern programming and database querying languages.
- Extensive experience with SQL and NoSQL databases, data lakes, and AWS S3, including the design and implementation of scalable data storage architectures.
- Proven track record in deploying and managing applications using AWS services such as EMR, EKS/ECS, EC2, Event Bus, and S3.
- Deep familiarity with enterprise logging and monitoring platforms, including Splunk, Elastic Search, Dynatrace, AppDynamics, Grafana, CloudWatch, and Datadog.
- Strong experience with Continuous Integration and Continuous Deployment processes, utilizing tools such as Jenkins and Spinnaker to automate and streamline software delivery pipelines.
- Comprehensive understanding of the full software development lifecycle, agile methodologies, application resiliency, and security best practices.
- Demonstrated expertise in software applications and technical processes within disciplines such as cloud computing, artificial intelligence, machine learning, and mobile technologies.