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Mid-level role with strong brand but niche Databricks/PySpark specialization moderates applicant density.
Transferable to other data teams but Databricks/PySpark specialization limits cross-industry fit.
Requires explicit 3-6 years plus mandatory Databricks, PySpark, SQL, and data validation expertise.
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Own end-to-end data quality validation for Databricks batch pipelines across Bronze, Silver, and Gold layers, ensuring accuracy, completeness, and consistency.
Design, develop, and maintain scalable data validation and automated testing frameworks using PySpark, Python, and Databricks SQL.
Manage test cases, execution, defect tracking with Xray and Jira, collaborating with cross-functional teams to enforce data quality standards and communicate risks.
3-6 years experience in Software Quality Assurance or Data Quality Engineering in large-scale enterprise data platforms.
Strong hands-on experience with Databricks SQL, Python, PySpark, and Medallion architecture (Bronze, Silver, Gold).
Bachelor’s degree in Computer Science, IT, Engineering, or related field.
Experience working in Agile or Scrum environments; proficiency with Xray and Jira for test management and defect tracking.
Experienced in building automated testing frameworks for data validation in modern data ecosystems using Databricks and Delta Lake.
Comfortable driving quality assurance in large-scale data platforms with a focus on integration, regression, and end-to-end testing.
Familiar with AI concepts and ethically applying AI tools to improve testing workflows and operational efficiency.