Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets to continuously improve software applications and systems
Identify hidden problems and patterns in data, using insights to improve coding hygiene and system architecture
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
Contribute to a team culture of diversity, opportunity, inclusion, and respect
Required Qualifications, Capabilities, and Skills
Formal training or certification in software engineering concepts
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
Proficiency in Java technologies and frameworks such as Spring and Spring Boot, and REST-based services
Proficiency in Unix/Linux environments, including the ability to navigate systems, investigate processes, and analyze logs
Hands-on practical experience in system design, application development, testing, and operational stability
Experience developing, debugging, and maintaining code in a large-scale environment using one or more modern programming languages and database querying languages
Understanding of agile methodologies, including continuous integration/continuous 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
Preferred Qualifications, Capabilities, and Skills
Familiarity with modern front-end technologies and frameworks
Exposure to cloud technologies and observability platforms such as Splunk and Grafana
Experience working with distributed messaging systems such as Apache Kafka
Knowledge of microservices architecture and containerization technologies
