QA ETL Testing

Matrix Global•Jacksonville, FL

About The Position

Matrix is seeking an experienced ETL Tester / Data Quality Test Engineer to validate data extraction, transformation, and loading processes and ensure accurate, complete, consistent, and reliable data across enterprise data platforms. The role works closely with data engineers, business analysts, and QA teams to identify data issues, validate business rules, and support high-quality data delivery for reporting and analytics. The position includes designing and executing tests for ETL/ELT pipelines and data integration workflows; validating source-to-target data flows, transformations, business rules, and data quality; and performing backend testing for data warehouses, data marts, and analytical reporting solutions. The tester will investigate defects, verify fixes, maintain test documentation, support multiple testing phases, and help improve data testing and automation processes. Matrix is a global technology services and consulting company with more than 17,000 employees worldwide. It provides technology, software, digital product, infrastructure, consulting, outsourcing, training, and implementation services across a range of industries. The company describes its culture as diverse and inclusive, with a challenging and engaging work environment, competitive compensation and benefits, and career development opportunities.

Requirements

At least 5 years of experience in ETL testing, data warehouse testing, data validation, or database testing.
Strong SQL proficiency, including complex joins, subqueries, CTEs, aggregations, window functions, and data reconciliation queries.
Hands-on experience testing ETL/ELT pipelines and validating source-to-target data flows.
Strong understanding of data warehousing concepts, dimensional modeling, fact and dimension tables, and star/snowflake schemas.
Experience validating data transformations, business rules, data mappings, and data integration logic.
Familiarity with relational databases such as SQL Server, Oracle, or similar platforms.
Experience identifying, documenting, tracking, and retesting data-related defects.
Strong analytical and problem-solving skills, attention to detail, and ability to investigate complex data issues.
Ability to collaborate with data engineering, QA, analytics, and business teams.
Experience with several of the listed technologies: advanced SQL and relational databases; ETL/ELT tools such as SSIS, Informatica, Talend, Azure Data Factory, or AWS Glue; data platforms such as Snowflake, Azure Synapse, or Amazon Redshift; BI tools such as Power BI or Tableau; test automation tools such as Selenium or Playwright or automated data validation frameworks; and test or defect management tools such as Jira or Azure DevOps.

Nice To Haves

Experience testing cloud-based data platforms and modern data pipelines.
Familiarity with data quality frameworks, automated reconciliation, and anomaly detection.
Experience validating APIs or data feeds integrated with ETL workflows.
Knowledge of CI/CD practices and automated testing in data engineering environments.
Experience testing large-scale datasets and distributed processing systems.
Familiarity with Agile/Scrum methodologies and standard QA processes.
Experience in banking, financial services, payments, or other data-intensive industries.

Responsibilities

Design, develop, and execute test plans, test cases, and test scenarios for ETL/ELT pipelines and data integration workflows.
Validate data extraction, transformation, and loading across source systems, staging environments, data warehouses, and target systems.
Perform source-to-target validation for data accuracy, completeness, consistency, and integrity.
Write complex SQL queries to verify transformations, reconcile data, and identify discrepancies.
Test data mapping documents, transformation logic, business rules, aggregations, calculations, and derived fields.
Validate data quality across large datasets, including duplicate records, missing values, incorrect formats, null values, and referential integrity issues.
Test incremental, full, and historical loads, as well as change data capture processes where applicable.
Perform backend testing for data warehouses, data marts, and analytical reporting solutions.
Validate BI report and dashboard data against underlying sources and business requirements.
Collaborate with data engineers and developers to investigate defects, identify root causes, and verify fixes.
Create and maintain test data, test scripts, test evidence, defect reports, and test execution documentation.
Support regression, integration, system, and user acceptance testing as required.
Contribute to improvements in data testing processes, reusable validation scripts, and test automation.