Data Engineering

Requirements & Design Review: Review business requirements, source-to-target mappings, and technical design documents to identify validation needs, test coverage gaps, and quality risks, and provide timely engineering feedback.
•Test Planning & Scenario Coverage: Review requirements, source-to-target mappings, and technical designs to identify test scenarios, edge cases, validation rules, and quality risks.
•Data Testing & Validation: Write and execute SQL-based data validations, reconcile source and target data, validate transformation logic, support functional and regression testing, and help resolve defects through root-cause analysis.
•Test Automation: Develop, maintain, and enhance automation scripts and reusable test assets to improve validation coverage, repeatability, and release confidence across data engineering workflows.
•AI-Assisted Automation Development: Use AI-assisted tools and techniques responsibly to accelerate development of automation scripts, improve test design efficiency, and strengthen validation productivity while adhering to enterprise engineering and control standards.
•Defect & Release Support: Coordinate with cross-functional teams during test cycles and releases, track defects, validate fixes, and ensure timely completion of assigned testing and quality assurance activities.
•Documentation & Test Standards: Maintain test documentation, validation evidence, reusable QA assets, and engineering artifacts in line with Schwab delivery and control standards.