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, using insights to drive improvements to coding hygiene and system architecture
Contribute to software engineering communities of practice and events that explore new and emerging technologies
Foster a team culture of diversity, opportunity, inclusion, and respect
Leverages 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
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
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 coding in one or more programming languages
Experience developing, debugging, and maintaining code in a large-scale environment using one or more modern programming languages and database querying languages
Solid understanding of the Software Development Life Cycle
Working knowledge of agile methodologies such as continuous integration/continuous delivery, application resiliency, and security
Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, Databricks, PySpark, artificial intelligence, machine learning, mobile, etc.)
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
Exposure to cloud technologies such as Amazon Web Services or equivalent cloud platforms
Experience with Databricks and agentic AI solutions
Familiarity with modern front-end technologies and frameworks
Experience working in a large enterprise environment with cross-functional engineering teams
