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Über
$102.50
Location - Cincinnati
About the Role
We are looking for a Senior Data QA Engineer to lead the quality assurance efforts for our Enterprise Retail Solutions data platform. This role is central to validating the integrity, accuracy, and completeness of data as it flows through complex ETL pipelines and integrations with systems like Oracle RMS.
You will work closely with data engineers, analysts, and business stakeholders to define test strategies, create automated and manual validation suites, and ensure all data pipelines meet functional and performance expectations across multiple integration points.
Required Qualifications
• 6+ years of experience in data QA, ETL testing, or data validation roles.
• Strong command of SQL and experience in testing data pipelines across structured and semi-structured formats.
• Hands-on experience with ETL tools like Informatica, Azure Data Factory, or custom ETL frameworks.
• Proficient in Azure Databricks, Azure Synapse, or similar cloud-based analytics platforms.
• Experience validating data flows into enterprise systems like Oracle RMS or similar ERP systems.
• Familiarity with version control, CI/CD tools (e.g., Azure DevOps), and test automation strategies.
• Deep understanding of data integrity, referential checks, boundary conditions, and negative testing.
• Excellent analytical and communication skills for working across engineering, QA, and business teams.
Preferred Qualifications
• Experience building automated QA pipelines using Python, dbt, or similar tools.
• Familiarity with data governance practices and validation of role-based access/security rules.
• Exposure to Agile, SCRUM, or SAFe frameworks for enterprise delivery.
• Understanding of data lineage, metadata tracking, and test data management tools.
Why Join Us
• Lead data quality for one of the most critical retail integration programs in a Fortune 25 enterprise.
• Work with a highly collaborative team driving modernization of data architecture and governance.
• Get exposure to large-scale Azure data ecosystems and advanced testing methodologies.
• Help shape quality engineering practices that ensure trusted and compliant data delivery.
Key Responsibilities
• Develop and execute comprehensive test strategies and test plans for validating ETL jobs, data transformations, and end-to-end system integrations.
• Lead data validation, reconciliation, and quality checks across boundary systems and enterprise retail platforms.
• Design and maintain automated data QA frameworks using tools like PyTest, dbt tests, or custom SQL/Python-based solutions.
• Write and run SQL queries and scripts to validate data movement, transformations, and mappings.
• Collaborate with data engineers and analysts to identify edge cases, define test datasets, and ensure coverage for functional and non-functional requirements.
• Create and maintain test cases, traceability matrices, and defect reports using tools such as JIRA and Confluence.
• Lead performance and regression testing of critical data pipelines and integration layers.
• Ensure compliance with data governance, security, and audit requirements through validation protocols.
• Participate in Agile ceremonies and represent QA readiness for user stories and feature releases.
Sprachkenntnisse
- English
Hinweis für Nutzer
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