Java Lead Software Engineer

Join us to shape the future of payments technology and regulatory reporting. You will have the opportunity to work with cutting-edge cloud platforms and data engineering tools, making a real impact on our business and your career growth. We value your expertise and encourage you to bring your ideas to a team that thrives on innovation and collaboration. At JPMorganChase, you will be part of a culture that supports diversity, inclusion, and continuous learning. Take the next step in your journey with us and help deliver trusted technology solutions. As a Java

Lead Software Engineer at JPMorganChase within the Payments Technology Regulatory Reporting team, you will design and deliver secure, scalable cloud technology products. You will collaborate with agile teams to create solutions that support our business objectives and drive continuous improvement. Your work will span multiple technical areas, allowing you to contribute to both team and firm-wide goals. You will help foster a culture of inclusion, respect, and opportunity while advancing your skills in a dynamic environment.

Execute end-to-end software delivery including design, development, testing, deployment, and production troubleshooting for Java/Spring Boot services and supporting components

• Build and maintain secure, scalable backend services and APIs with a strong focus on performance, resiliency, and long-term maintainability
• Create design artifacts for complex workflows, covering processing logic, dependency management, failure handling, service-level objectives, and resiliency patterns
• Apply distributed systems best practices including idempotency, retries, timeouts, back-pressure, and graceful degradation
• Collaborate with product, quality assurance, site reliability engineering, and partner engineering teams to deliver high-quality, production-ready solutions
• Improve engineering standards through code reviews, test strategy, continuous integration and delivery improvements, observability, and operational readiness
• Contribute to internal libraries and engineering communities; evaluate and adopt emerging technologies where appropriate to drive team productivity
• Leverage enterprise-authorized AI-assisted software development tools to improve code quality, delivery speed, and productivity, while validating outputs through peer review, automated testing, and secure coding standards
• Apply 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

, capabilities, and skills

• Formal training or certification on software engineering concepts and applied experience
• Strong hands-on experience in Java backend development and Spring Boot in production environments
• Proven ability in system design for scalable, secure services using microservices and API-driven architectures
• Strong engineering fundamentals including object-oriented programming, design patterns, concurrency, debugging, and performance tuning
• Solid testing discipline encompassing unit and integration testing, automation, and code quality practices
• Experience with relational databases and SQL, including understanding of data modeling and transactional concepts
• Familiarity with agile delivery and the ability to develop within enterprise controls and standards
• Working knowledge of continuous integration and delivery, application resiliency, security best practices, and observability and monitoring
• Hands-on experience using enterprise-authorized AI-assisted software development tools with demonstrated ability to critically evaluate and validate AI-generated outputs
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs and outputs, and adherence to resiliency and security expectations Preferred qualifications, capabilities, and skills
• Experience supporting data processing and integration work using Python and/or PySpark, such as batch jobs, reconciliation pipelines, or platform integrations
• Experience with data engineering and orchestration tooling such as Databricks and Airflow or similar platforms
• Exposure to cloud services such as managed streaming for Apache Kafka, compute, container orchestration, object storage, managed databases, and serverless functions
• Experience with streaming and event-driven systems, particularly in payments or financial services contexts
• Familiarity with payment regulatory reporting requirements and associated data workflows

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