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As an AWS Lead Software Engineer-Java/Spring Boot at JPMorgan Chase within Commercial & Investment Bank's Post Trade Technology Reporting 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. This group is going through a migration and modernization journey where you will be contributing to designing products, owning the design end-to-end, designing & building applications, and other technical leadership activities.

Job responsibilities

• Develop code that conforms to the design, addresses non-functional requirements, and complies with the coding standards and conventions
• Create detailed Unit Test cases, perform unit testing, integration testing, support QA and User acceptance testing and also address ad hoc user queries
• Practice test-driven development, automated testing and continuous integration
• Actively participate in peer review sessions by evaluating code quality, ensuring adherence to best practices, identifying defects, and providing constructive, actionable feedback to improve overall team deliverables
• 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.
• Mentor and support junior developers by offering technical guidance, conducting knowledge-sharing sessions, reviewing their work, and fostering skill development to strengthen team capabilities
• Analyze programs and troubleshoot production issues. Identify potential performance tuning improvement areas and implement tuning measures
• Collaborate with regional and global team members from other regions

• Possess a good understanding of change management and release processes

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience

• Hands-on experience coding in one or more programming languages, including Java 

• Experience designing and implementing applications using Spring Boot

• Hands-on experience with AWS Cloud technologies

• Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages

• Overall knowledge of the Software Development Life Cycle

• 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

• Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

• Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

Preferred qualifications, capabilities, and skills

• Proven experience designing and building scalable big data warehouses using Apache Spark, including data ingestion, transformation, and optimization for large-scale datasets

• Working knowledge of containerization and orchestration tools such as Kubernetes and Docker, along with experience leveraging Databricks for distributed data processes and analytics

• Hands-on experience working with AWS cloud services (e.g., S3, EC2, EMR), NoSQL databases such as MongoDB, and modern data lake technologies including Apache Iceberg for efficient data storage and management