Apply reuse-first, AI-assisted practices to strengthen software development lifecycle quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability, auditability, and alignment to resiliency and security expectations
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
Good working knowledge of cloud-based data services (especially Glue jobs and Federated Data Lake), unified analytics platforms, and Python
Experience across the data lifecycle, including ingestion, transformation, storage, and access patterns
Advanced proficiency in SQL, including joins and aggregations, with a working understanding of NoSQL databases
Significant experience with statistical data analysis and the ability to determine appropriate tools and data patterns for analysis
Experience utilizing cloud services for developing, deploying, and managing applications at scale
Good understanding and working knowledge of software development lifecycle tools used for configuration management, continuous integration and delivery pipelines, unit testing, regression testing, and performance testing
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 standardized data layer practices such as Medallion architecture
Exposure to relational database platforms and cloud data warehousing solutions
Curiosity and foundational understanding of generative AI, large language models, and AI/ML solutions
Skills in designing efficient data models, including normalization, denormalization, and schema design, with an understanding of relational and star schemas
