We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III at JPMorganChase within the Data Science Engineering team in Asset & Wealth Management, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

• Execute software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Create secure and high-quality production code and maintain algorithms that run synchronously with appropriate systems
• Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), 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
• Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
• Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
• Proactively identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene and system architecture

• Formal training or certification on software engineering concepts and proficient applied experience
• Proficiency in Python is essential, with strong working knowledge of SQL, PySpark, Snowpark, or similar tools for data engineering and analytical workflows
• Hands-on practical experience in system design, application development, testing, and operational stability
• 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
• Overall knowledge of the Software Development Life Cycle
• Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

• Degree in a STEM subject
• Exposure to cloud technologies
• Knowledge of financial markets, particularly equities, is advantageous but not required