Sr Manager, Data Engineering Senior

Key Responsibilities

Lead the team technically to resolve issues and eliminate roadblocks for execution and delivery • 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.

Required Qualification:

Ideal Candidate Profile • 15+ 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. ▪ Architects advanced processes to manage risks and regulate modifications per policy ▪ Uncovers complex phenomena through sophisticated data analysis ▪ Establishes robust processes to create, secure, and monitor data pipelines and stores ▪ Optimizes processes for effective data asset governance ▪ Leverages robust models and diagrams to convey data requirements ▪ Develops QA processes that confirm compliance and ensure operational reliability Skills and Attributes • Technical Depth: Strong hands-on capability in data testing, validation, automation script development, and problem solving across enterprise data workflows. • Quality Mindset: Focused on accuracy, completeness, traceability, and reliability across testing, validation, and release activities. • Execution Focus: Able to prioritize effectively, work independently, and consistently deliver in a dynamic environment. • Collaboration: Works well with cross-functional teams and communicates issues, risks, and progress clearly. • Continuous Improvement: Looks for opportunities to improve test automation, leverage AI-assisted development responsibly, streamline validation processes, and strengthen quality engineering practices.