Company
Technology
QAAutomationEngineer–EnterpriseData&AI
Neural analysis suggests this role is
optimal for Senior candidates.
“QA Automation Engineer – Enterprise Data & AI. Skills: QA Automation, Data Validation, Enterprise Data. Execute automated data validation tests. Extend automated data validation tests”
Industry & Context.
Analytical skills; Debugging skills; Problem-solving skills
What They're Looking For.
Must Have
5+ years QA automation, 5+ years SDET, 5+ years data validation, Databricks experience, Notebook development, Data pipeline validation, Advanced Python, Advanced PySpark, Advanced SQL, Data processing workflows, Data reconciliation experience, Large-scale data validation, Data quality frameworks experience, CI/CD pipelines familiarity, Azure DevOps familiarity, Analytical skills, Debugging skills, Problem-solving skills, Attention to detail, Effective collaboration
Nice to Have
Azure Purview experience, Profisee MDM experience
What You'll Do.
Execute automated data validation tests
Extend automated data validation tests
Validate end-to-end data pipelines
Perform data reconciliation
Enhance data quality frameworks
Maintain data quality frameworks
Implement validation checks
Monitor validation checks
Develop automated test scripts
Integrate automated tests
Support testing activities
How You'll Work.
Team & Collaboration
Data engineering teams; Analytics teams; Cross-functional stakeholders
Full Job Description
## Accountabilities Execute and extend automated data validation tests within Databricks using Python, PySpark, SQL, and notebook-based frameworks. Validate end-to-end data pipelines, including ingestion, batch and incremental loads, transformations, joins, and business rule accuracy. Perform data reconciliation between source systems and target datasets to ensure completeness and consistency. Enhance and maintain existing data quality frameworks, including rule sets for accuracy, completeness, and reliability. Implement and monitor validation checks, thresholds, alerts, and exception handling mechanisms. Develop reusable and scalable automated test scripts aligned with enterprise data testing standards. Integrate automated tests into CI/CD pipelines (e.g., Azure DevOps) and ensure reliable execution across environments. Support testing activities across QA and staging environments, including defect triage and root cause analysis. Collaborate with data engineering and analytics teams to ensure data integrity for reporting and visualization tools such as Tableau. Requirements: 5+ years of experience in QA automation, SDET, or data validation engineering roles. Strong hands-on experience with Databricks, including notebook development and data pipeline validation. Advanced proficiency in Python, PySpark, SQL, and data processing workflows. Proven experience in data reconciliation and large-scale data validation across enterprise systems. Experience building, extending, or maintaining data quality frameworks in complex environments. Familiarity with CI/CD pipelines such as Azure DevOps for test integration and execution. Strong analytical, debugging, and problem-solving skills with attention to detail. Ability to collaborate effectively with data engineers, QA teams, and cross-functional stakeholders. Experience with tools such as Azure Purview or Profisee MDM is a plus. Benefits: Competitive compensation aligned with experience and expertise. Fully remote opportunity
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