Sr IT QA Data Quality Analyst

Guild Mortgage Company
, closing loans and opening doors since 1960. As a mortgage banking firm, we are dedicated to serving the homeowner/buyer. Our goal is to provide affordable home financing for our customers, utilizing the best terms available while providing a level of professionalism and service unsurpassed in the lending industry.

The Sr. IT QA Data Quality Analyst plays an important role in the organization by leading quality assurance activities for Enterprise Data Warehouse (EDW), Data Services, and related data delivery initiatives. This role is responsible for defining and maintaining Data QA strategy, repeatable test approaches, EDW quality governance practices, and release readiness standards to ensure data delivered to stakeholders is complete, accurate, timely, traceable, and fit for business use.

The role documents, executes, and oversees data quality checks across data assets throughout EDW delivery layers, including validation of data repositories against external source systems, source-to-target mappings, transformation rules, Change Data Capture (CDC), dimensional models, data masking, downstream reporting impacts, and data quality metrics. The Sr. IT QA Data Quality Analyst partners cross-functionally with Data Engineering, Data Ops, BI Engineering, Product, Business Analysts, UAT teams, vendors, end users, and Project Management to bring a QA perspective to planning, execution, defect resolution, release readiness, and continuous improvement.

This position also mentors QA Analysts, reviews test coverage and evidence, establishes reusable QA standards, identifies opportunities to automate repeatable data comparisons and regression checks, and provides evidence-based risk assessments and go/no-go recommendations for data releases.

This role is an exempt position with a targeted salary range of $82,506 to $130,000 annually.

Compensation at Guild is influenced by a wide array of factors including but not limited to local and federal minimum wage requirements, education, level of experience, and applicant’s geographical location.

• Define, maintain, and continuously improve the Data QA strategy for EDW and Data Services initiatives, including risk-based testing approaches, validation standards, quality gates, and release readiness expectations.
• Design, develop, document, and perform data quality checks and maintain data quality assurance throughout the Enterprise Data Warehouse.
• Establish testing entry, exit, suspension, and completion criteria for data delivery initiatives.
• Develop repeatable test plans for EDW projects, including validation of source-to-target mappings, business rules, transformation logic, referential integrity, duplicates, null handling, key relationships, data completeness, and data accuracy.
• Validate Change Data Capture (CDC) processing, including inserts, updates, deletes, incremental loads, historical data processing, and reconciliation between source and target systems.
• Validate data masking, sensitive data handling, and privacy-related transformation rules in partnership with appropriate technical and business stakeholders.
• Review test coverage to ensure sufficient validation of requirements, source data, target data, transformation logic, dimensional models, downstream reporting, and production readiness risks.
• Work in conjunction with Data Engineers and BI Engineers to model, calculate, and track data quality results.
• Write SQL and other reports to evaluate and analyze data content at platform levels related to quality assurance; this does not include actual business report development.
• Perform source-to-target reconciliation and data validation across operational systems, cloud data platforms, EDW layers, and downstream reporting or analytics products.
• Create BI dashboards to highlight data quality content, overall population metrics, defect trends, testing progress, and release readiness indicators.
• Analyze incoming data feeds for completeness, content, timeliness, accuracy, and data expectations.
• Identify trends and analysis for data content across time and other dimensions.
• Analyze data content and identify gaps…