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Metro location, mid-level (3–5yrs), and generic QA title increase competition, but Databricks niche reduces pool.
Databricks and ETL-focused testing creates strong data-platform bias, limiting cross-industry transferability.
Explicit 3–5 years plus mandatory Databricks, SQL and PySpark requirements.
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Validate data accuracy, completeness, and consistency in Databricks Delta tables across Bronze, Silver, and Gold layers.
Perform source-to-target data validation, reconciliation, and test data transformations, business rules, and aggregation logic.
Create and execute SQL test cases, maintain test scenarios, track defects using JIRA, and participate in Agile ceremonies to ensure quality of data pipelines.
3–5 years of experience in Data Testing, ETL Testing, or Data Validation.
Hands-on experience with Databricks, Delta Lake, and strong proficiency in SQL for data validation.
Experience using JIRA for defect tracking and test management.
Experience working in Agile/Scrum environments.
Experienced in validating complex ETL/ELT pipelines and data transformations within Databricks environment.
Familiar with data warehousing concepts, Spark SQL, Databricks notebooks, and Azure Databricks ecosystem.
Comfortable working in Agile teams with defect management and test execution responsibilities using SQL-centric and test automation tools.