Lead Software Engineer - Java, AWS, Databricks

Are you ready to push the boundaries of what’s possible? At JPMorganChase, you’ll have the chance to make a real impact while growing your skills and advancing your career. We value creativity, collaboration, and a passion for technology. Join a team where your ideas matter and your contributions drive meaningful change. Experience the rewards of working in a supportive, forward-thinking environment. Job Summary: As a Lead Software Engineer in the Corporate Technology team, you will play a key role in delivering trusted, market-leading technology products. You will work within an agile team to enhance, build, and support secure, stable, and scalable solutions. You will help shape the team culture and drive impactful results across multiple technical areas. Your expertise will support the firm’s business objectives and foster innovation. Job Responsibilities: Execute creative software solutions, design, development, and technical troubleshooting, thinking beyond routine approaches to solve complex problems. Develop secure, high-quality production code, and review and debug code written by others. Drive 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. 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. Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems. Lead evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture. Required Qualifications, Capabilities, and Skills: Formal training or certification on software engineering concepts and advanced applied experience. Hands-on practical experience delivering system design, application development, testing, and operational stability. Advanced in one or more programming languages, preferrably Java. Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security. Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices. Proficient in all aspects of the Software Development Life Cycle. Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security. In-depth knowledge of the financial services industry and their IT systems. Practical cloud native experience. Preferred Qualifications, Capabilities, and Skills: Experience working in big data environments with structured, semi-structured, and unstructured data. Hands-on practical experience in system design, application development, testing, and operational stability. Advanced knowledge of Java programming language. In-depth knowledge of software applications and technical processes in areas such as cloud, AI, machine learning, or mobile. Ability to tackle design and functionality problems independently with minimal oversight. Proficiency in application, data, and infrastructure architecture disciplines. Proficiency in cloud-native microservices architecture, design, and implementation. Proficiency in event-driven architecture and application containerization. Experience building applications on public cloud platforms like AWS, GCP, and Azure, with a preference for AWS. Proficiency in real-time streaming applications using Apache Spark Streaming, Apache Kafka, Amazon Kinesis, and emerging cloud serverless managed services.