Lead Software Engineer

JPMorganChase•Chicago, IL

About The Position

As a Lead Software Engineer in JPMorganChase’s Consumer Community Banking Digital Technology department, you will contribute to an agile team building and enhancing trusted technology products. You will deliver secure, stable, and scalable solutions across technical areas in support of business objectives, including backend, cloud, big data, and Java solutions. The role includes designing and developing software, producing architecture and design artifacts, and maintaining production code and application operations. You will use enterprise-authorized AI-assisted development tools to support coding, testing, troubleshooting, and documentation, while reviewing and validating their outputs for correctness, performance, and security. You will work in an Agile/Scrum environment, contribute to continuous improvement, and promote design, architecture, and AWS cloud-native best practices. You will also help improve operational stability by identifying opportunities to automate manual processes and address recurring issues.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability.
Experience with a distributed streaming platform such as Kafka.
Proficiency in one or more programming languages, including Core Java and Spring Boot.
Demonstrated technical knowledge in areas including big data technologies (Spark, S3, Flink), cloud technology and AWS, Databricks, Core Java, Spring Boot, and artificial intelligence.
Strong understanding of data, data modeling, database methodologies, and architecture disciplines.
Advanced SQL and data management knowledge.
Hands-on experience with enterprise-authorized AI-assisted software development tools for tasks such as coding, test creation, troubleshooting, or documentation, and ability to critically evaluate, validate, and refine generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations; ability to guide peers on safe and effective use.
Experience developing, debugging, and maintaining code in a large corporate environment using one or more modern programming languages and database querying languages.
Experience with AI tools such as Claude or GitHub Copilot.

Nice To Haves

AWS Cloud Certification and other relevant certifications.
Data engineering experience.

Responsibilities

Design, develop, and deliver software solutions, including breaking down complex technical problems and considering approaches beyond routine solutions.
Troubleshoot and tune the performance of backend workflows using technologies such as Java, Spring Boot, AWS, and PostgreSQL.
Create secure, high-quality production code and maintain algorithms that run synchronously with appropriate systems.
Develop, support, and troubleshoot cloud, big data, and Java solutions to meet business and technology requirements.
Produce architecture and design artifacts for complex applications and ensure software development meets design constraints.
Use enterprise-authorized AI coding-assistance tools to improve code quality, delivery speed, and productivity; validate outputs through peer review, automated testing, and secure coding standards.
Share learnings and reusable AI-assisted development patterns to improve team effectiveness.
Use software development lifecycle tools, including authorized AI-assisted development and automation capabilities, to improve the value of automation.
Gather, analyze, synthesize, and visualize large, diverse data sets to support continuous improvement of software applications and systems.
Contribute to sprint planning, code reviews, and continuous improvement in an Agile/Scrum environment.
Promote best practices in design, architecture, and AWS cloud-native development.
Identify opportunities to eliminate or automate manual processes and recurring issues to improve platform operational stability.