Job responsibilities
Execute software solutions across design, development, and technical troubleshooting, thinking beyond conventional approaches to solve complex engineering challenges
Create secure, high-quality production code and maintain algorithms that operate synchronously with critical financial systems
Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets to drive continuous improvement of software applications
Proactively identify hidden problems and patterns in data, using insights to improve coding hygiene and system architecture
Contribute to software engineering communities of practice and events that explore new and emerging technologies
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
Champion a team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and proficient applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Proficiency in Java 8 and above, including strong working knowledge of the Java ecosystem
Solid understanding of the Spring framework and its ecosystem
Hands-on practical experience with relational database management systems
Experience developing, debugging, and maintaining code in a large-scale environment using modern programming languages and database querying languages
Demonstrated understanding of the Software Development Life Cycle and agile methodologies, including continuous integration and delivery, application resiliency, and security practices
Demonstrated knowledge of software applications and technical processes within a technical discipline such as cloud, artificial intelligence, machine learning, or mobile
Hands-on experience with Unix/Linux operating systems
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) 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/outputs, and adherence to resiliency and security expectations
Preferred qualifications, capabilities, and skills
Familiarity with modern front-end technologies such as JavaScript and React
Experience with DevOps practices including continuous integration and continuous deployment tools such as Jenkins and SonarQube
Exposure to job scheduling tools such as Autosys or Control-M
Exposure to cloud technologies and cloud-native development patterns
Exposure to NoSQL databases
