Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Contributes to software engineering communities of practice and events that explore new and emerging technologies
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
Applies 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.
Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Experience in developing, debugging, and maintaining code using Java Spring/Spring Boot in Microservices Application.
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Experience working with databases like Oracle and messaging frameworks like Kafka
Customer focus, excellent problem solving, and analytical skills with good verbal and written communication skills.
Ability to work independently, plan, organize, prioritize, and meet deadlines in a rapidly changing environment
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
Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., AWS cloud,docker,k8s, artificial intelligence, machine learning, etc.)
Preferred qualifications, capabilities, and skills
Excellent analytical and problem-solving skills
Familiarity with Healthcare domain
Exposure to cloud technologies
