Specialist - 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.

• 3+ years of overall experience with strong hands-on expertise in data engineering testing, ETL validation, QA automation, or data warehouse environments.
• Experience working on large-scale data testing or data engineering quality assurance initiatives with strong hands-on ownership of automation development in an Agile delivery model.
• Strong foundation in data validation, defect identification, troubleshooting, and quality engineering best practices across testing lifecycles.
• Ability to work collaboratively across product, development, and QA teams in a fast-paced enterprise setting. Required Experience
• Hands-on experience in Data Warehouse and ETL testing, validation, or quality engineering, with demonstrated ability to develop automation scripts for test execution, reconciliation, and validation, preferably using IICS or PowerCenter.
• Experience with Google Cloud Platform (GCP) and modern cloud-based data engineering practices.
• Proficiency in SQL, Python, and basic Unix/shell scripting to develop automation scripts and support testing, validation, automation, and troubleshooting.
• Experience using AI-assisted engineering tools to help create, refine, and optimize automation scripts and testing accelerators while applying sound engineering judgment, validation, and governance.
• Experience with Agile delivery tools such as Jira or Rally, with a solid understanding of software development lifecycle practices.
• Strong analytical, troubleshooting, defect diagnosis, test evidence review, and root-cause analysis skills.
• Ability to manage priorities independently, communicate clearly, and deliver high-quality work within tight timelines

Working knowledge of CI/CD and DevOps tools such as Bamboo, Bitbucket, GitHub, or GitHub Actions