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Job description:
Job description:
- We are seeking a QA Automation Engineer / SDET to support quality engineering for our Enterprise Data Platform.
- This role will focus on validating data pipelines and extending existing data quality frameworks within Databricks, ensuring data accuracy, quality, completeness, and reliability across the data lifecycle.
- This role will play a key part in implementing, enhancing, and executing data validation and data quality checks across the medallion architecture.
- Execute and extend existing automated tests within Databricks using PySpark, Python, SQL, and notebooks to validate data pipelines
- Perform data reconciliation between source systems and target datasets
- Validate ingestion processes including batch and incremental loads
- Test transformations, joins, aggregations, and business rules for accuracy
- Extend and enhance existing data quality frameworks and rule sets
- Implement validation checks for data completeness, accuracy, consistency, and quality.
- Validate thresholds, alerts, and exception handling mechanisms
- Support tracking of data quality metrics and trends
- Develop and maintain reusable and scalable test scripts aligned with existing frameworks
- Integrate and execute tests within CI/CD pipelines (e.g., Azure DevOps)
- Support testing activities across environments (QA, Staging)
- Ensure consistent and reliable execution of automated tests
- Partner closely with Data Engineers and Data Quality Engineers to identify, troubleshoot, and resolve data issues
- Participate in Agile ceremonies and contribute to sprint deliverables
- Support defect triage, root cause analysis, and retesting
- Ensure data accuracy and consistency for downstream consumption and business reporting in data visualization tools such as Tableau.
- 5 years of experience in data validation, QA engineering, or SDET roles
- Hands-on experience with creating Databricks notebooks and data pipeline validation
- Strong proficiency in PySpark, Python, SQL, and Databricks notebooks
- Experience working with and extending existing data validation or data quality frameworks
- Strong experience in data reconciliation and large-scale data validation
- Experience executing tests within CI/CD pipelines (e.g., Azure DevOps)
- Strong analytical and problem-solving skills
- Experience with tools such as Azure Purview and Profisee MDM is preferred.