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 home owner/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 IT QA Data Quality Analyst plays an important role in the organization by performing quality assurance activities for Enterprise Data Warehouse (EDW), Data Services, and related data delivery initiatives. The role develops and executes repeatable data validation approaches to help ensure data delivered to stakeholders is complete, accurate, timely, traceable, and fit for business use.

The analyst documents and performs data quality checks across data assets and delivery layers, including validation of source-to-target mappings, transformation rules, Change Data Capture (CDC), dimensional models, data masking, downstream reporting impacts, and data quality metrics. The role works cross-functionally with Data Engineering, Data Ops, BI Engineering, Product, Business Analysts, UAT teams, vendors, end users, and Project Management to support test planning, execution, defect resolution, release readiness, and continuous improvement.

This is an individual-contributor role. The analyst maintains test evidence, identifies opportunities to automate repeatable validation and regression checks, communicates data quality risks, and supports evidence-based release decisions under established QA standards and direction.

This role is an exempt position with a targeted salary range of $82,506.00 - $.

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.

• Execute and continuously improve repeatable Data QA approaches for EDW and Data Services initiatives using established risk-based testing, validation standards, quality gates, and release readiness expectations.
• Design, develop, document, and perform data quality checks throughout the Enterprise Data Warehouse.
• Apply established testing entry, exit, suspension, and completion criteria to assigned data delivery initiatives.
• Develop and execute test plans that validate source-to-target mappings, business rules, transformation logic, referential integrity, duplicates, null handling, key relationships, data completeness, and data accuracy.
• Validate 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 with appropriate technical and business stakeholders.
• Perform source-to-target reconciliation and data validation across operational systems, Microsoft/Azure data platforms, EDW layers, cloud storage, pipelines, and downstream reporting or analytics products.
• Write SQL and other quality-assurance reports to evaluate and analyze data content at platform levels; this does not include business report development.
• Work with Data Engineers and BI Engineers to model, calculate, and track data quality results.
• Create or maintain BI dashboards that show data quality measures, population metrics, defect trends, testing progress, and release readiness indicators.
• Analyze incoming data feeds for completeness, content, timeliness, accuracy, and alignment with data expectations; identify trends across time and other dimensions.
• Identify gaps in metadata, reference data, standardization, business rules, and data quality controls.
• Create alert mechanisms for system issues, data quality issues, data receipt, completeness, and expected schedule variances.
• Partner with Product, Development, Data Engineering, Data Ops, BI Engineering, UAT, business stakeholders, and vendors to support certification of data.
• Create and maintain requirements traceability that connects requirements, source and target data, test cases, execution results, defects, and test evidence.
• Support defect analysis and root cause investigation with clear data evidence, reproduction steps, impacted records, business-rule context, and downstream-impact assessment.
• Perform regression testing for code releases, data pipeline changes, EDW enhancements, reporting changes, and other production-impacting data changes.
• Identify and implement…