Contribute to technical documentation, ensuring clarity and accuracy for internal and external stakeholders
Participate in agile ceremonies, providing input on sprint planning, estimation, and retrospectives
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
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience
Strong data modeling expertise with proven experience modeling tables using ERwin, including normalization and dimensional modeling techniques
Strong hands-on experience with Databricks as a data engineering platform, including building and optimizing ETL/ELT pipelines (performance tuning, partitioning, file sizing, and incremental loads)
Strong proficiency in SQL, including advanced techniques such as joins, analytics and window functions, and query optimization
Strong Oracle experience, including DDL/DML and schema design best practices, PL/SQL development (procedures, functions, and packages), and Oracle performance tuning (execution plans, indexes, partitioning, and statistics)
Experience handling JSON and semi-structured data, including parsing, flattening, and schema evolution considerations
Experience working with Databricks Genie Spaces and ThoughtSpot integration to Databricks
Proficiency with Git-based workflows using Bitbucket and/or GitHub, with comfort working in IntelliJ IDEA or similar integrated development environments
Strong problem-solving and debugging skills across ingestion, transformation, and serving layers
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
Preferred qualifications, capabilities, and skills
Experience enabling or administering Unity Catalog, including catalog and schema design, permissions management, and lineage and metadata patterns
Experience working in an agile or scrum delivery environment
Exposure to containerization and orchestration technologies such as Docker or Kubernetes
Familiarity with CI/CD tooling and DevOps practices
Knowledge of financial services technology or regulated industry environments
