Java Lead Software Engineer

Build secure, scalable Python services powering mission-critical regulatory reporting and shaping the future of payments technology.

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. Job responsibilities Execute end-to-end software delivery including design, development, testing, deployment, and technical troubleshooting for Python-based services, batch jobs, and data pipelines Write secure, high-quality production Python code with attention to performance, reliability, readability, and testing discipline Produce design artifacts for complex workflows covering pipeline orchestration, processing logic, dependency and failure handling, and service-level objectives Gather, analyze, and synthesize data to produce reports, metrics, and visualizations that support business and operational insights Identify hidden problems and patterns in data and pipeline execution — including data quality issues, drift, anomalies, and bottlenecks — to drive system improvements Work individually or as part of a distributed agile team to deliver projects on time with strong ownership and accountability Contribute to engineering communities, improve internal libraries and utilities, and evaluate emerging technologies where appropriate 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 Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and advanced applied experience Hands-on experience in system design, Python application development, testing, and operational stability in production environments Strong proficiency in Python including data processing, APIs and services, and scripting and automation using common libraries and frameworks relevant to the role Experience with data engineering and analytics platforms such as PySpark, Databricks, Airflow, or similar orchestration tools Experience with relational databases and SQL; familiarity with NoSQL databases and data modeling concepts Knowledge across the data lifecycle including ingestion, validation, transformation, storage, governance and lineage considerations, and consumption patterns Ability to develop, debug, and maintain code in large enterprise environments with established controls and standards Solid understanding of the Software Development Life Cycle and agile methodologies Familiarity with 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 Exposure to cloud technologies including managed data platforms, streaming services, container orchestration, object storage, managed databases, and serverless compute Experience with streaming and event-driven systems in payments or financial services contexts Familiarity with payment regulatory reporting requirements and associated data workflows