Sr IT QA Data Quality Analyst

Guild Mortgage•,

2d•$82,506 - $130,000•Onsite

About The Position

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, DataOps, 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.

Requirements

Bachelor's Degree directly related to the position or equivalent, preferred.
Minimum five years' experience with corporate data management systems in high-compliance contexts.
Knowledge of data quality concepts, including source-to-target mapping, data reconciliation, snowflake/star schema, dimensional modeling, slowly changing dimensions, referential integrity, completeness, correctness, consistency, validity, uniqueness, and timeliness.
Advanced SQL skills, including joins, aggregations, common table expressions, window functions, exception queries, reconciliation queries, duplicate analysis, null analysis, key validation, and transformation validation.
Knowledge of Snowflake and cloud-based data warehouse or data platform concepts, including the ability to create simple and complex validation queries.
Knowledge of AWS S3 and data file formats such as Parquet, Avro, and other structured or semi-structured formats.
Knowledge of Informatica Data Quality and IICS; experience creating data quality checks for correctness, completeness, consistency, validity, uniqueness, and other data quality dimensions.
Experience validating ETL/ELT pipelines, data ingestion, data transformation, data movement, data warehouse layers, dimensional models, and downstream reporting outputs.
Experience validating Change Data Capture (CDC), incremental loads, inserts, updates, deletes, historical data processing, and Type 2 slowly changing dimension behavior.
Experience performing source-to-target validation and reconciliation across operational source systems, enterprise data warehouses, cloud data platforms, and reporting or analytics products.
Experience validating data masking, sensitive data handling, privacy-related rules, and downstream reporting impacts.
Basic knowledge of statistical techniques such as Shewhart method, trend analysis, and other methods used to assess data quality patterns.
Experience with automating testing, SQL-based validation, data comparison, and regression testing for code releases.
Experience with test data management across multiple workstreams and working with DBAs or data platform teams on test data strategy.
Experience with effective defect management, defect analysis, root cause investigation, requirements traceability, and audit-ready evidence.
Experience using Jira and qTest, or equivalent tools, for test planning, test execution, defect tracking, traceability, and evidence management.
Experience implementing data quality metrics, dashboards, release readiness reporting, and evidencing for audit.
Ability to define QA strategy, establish entry and exit criteria, review test coverage, assess release risk, and provide evidence-based recommendations.
Ability to mentor other QA Analysts and promote consistent data QA practices across projects and workstreams.
Excellent verbal and written communication skills required.
Ability to think critically, including the ability to evaluate facts and data to draw conclusions, determine the downstream impact of decisions, and assess associated risks.
Ability to create clear, concise, and detail-oriented test plans, test cases, test evidence, defect documentation, and release readiness summaries.
Ability to solve technical problems and think abstractly.
Excellent verbal and written communication skills required.
Highly organized and detail-oriented; ability to work in a fast-paced, metrics-driven environment required.
Proficiency in Microsoft Office Suite, Word, Excel, Wiki, collaborative cloud-based programs, and third-party software applications required.
Commitment to company values.
Customer Service - Proactive attention to each person.
Integrity - Do and say what's right.
Respect - Treat others with dignity.
Collaboration - Listen and work together.
Learning - Seek knowledge and strive for improvement.
Excellence – Deliver the unexpected.

Responsibilities

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 in metadata, reference data, standardization, business rules, and data quality controls.
Work with members of the Data Engineering, DataOps, DevOps, BI Engineering, Product, and business teams to understand the content and quality of data required to support production deliveries.
Generate overall platform-level content and metrics for Power BI dashboards to provide visibility to Product teams and management of data completeness, data quality, defect trends, and release readiness within the EDW.
Create alert mechanisms for system issues, data quality issues, data receipt, data completeness, and expected calendar or schedule variances.
Serve as the liaison between Product, Development, Data Engineering, DataOps, BI Engineering, UAT teams, business stakeholders, and vendors to ensure proper certification of data.
Create and maintain requirement traceability matrices to map requirements, source data, target data, test cases, execution results, defects, and test evidence, working with Business Analysts and Data Analysts as needed.
Coordinate QA activities across project teams to ensure test planning, execution, defect triage, retesting, regression testing, and release readiness are completed effectively.
Provide risk assessments and evidence-based release readiness recommendations, including go/no-go input based on test results, defect status, data quality metrics, unresolved risks, and stakeholder impacts.
Mentor QA Analysts by reviewing test plans, test cases, SQL queries, execution evidence, defect documentation, and data validation approaches.
Develop, maintain, and promote reusable Data QA standards, templates, validation approaches, test evidence practices, and testing best practices across Data Services.
Identify and implement opportunities to automate repeatable SQL validation, source-to-target reconciliation, data comparison, regression testing, and quality reporting.
Support defect analysis and root cause investigation by providing 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.
Ensure test execution, defect tracking, traceability, and evidence are maintained in approved test management and work tracking tools such as qTest, Jira, or equivalent systems.
Perform other duties as assigned.