Lead Software Engineer Lead Software engineer - Java, Spring Boot, Kafka, AWS, Spark, Copilot/Claude/AI skills

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As a Lead Software Engineer at JPMorganChase within the Consumer and Community Banking - Trust and Security, 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 software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- 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.
- 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
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Excellent system design skills for designing distributed systems with scalability, resiliency, security, and performance best practices.
- Advanced proficiency in Java (17/21+) and Spring Boot for building scalable RESTful microservices.
Strong command of Kafka for event-driven architectures, including topic design, partitions, consumer groups, and delivery semantics.
- Strong experience in Java (Strong hands-on coding expertise), Spring Boot / Spring Framework, AWS Cloud and Kafka (Deep expertise in event streaming and messaging)
- In-depth knowledge of Cassandra for distributed data modeling, partition strategy, and query tuning at scale.
- Extensive experience with AWS data lake & ETL technologies (S3, Glue, EMR, Lambda, Step Functions) and Maven for build/dependency management.
- Solid expertise in containerization and orchestration using Docker and Kubernetes, deploying workloads on AWS EKS and ECS.
- Hands-on mastery of Apache Spark (batch + streaming) for large-scale data processing and performance optimization.Proven capability in Jenkins-based CI/CD automation, including pipelines, quality gates, artifact publishing, and deployments.
- Effective AI utilization to boost development productivity by leveraging AI agents/skills for coding assistance, test generation, refactoring, documentation, and faster troubleshooting.
- 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
- Familiarity with modern front-end technologies.
- Cassandra, Python