Databricks Tester (Data Validation)

Job Summary

We are looking for a Databricks Tester with 3-5 years of experience in data testing and validation. The ideal candidate should have hands-on experience validating data pipelines and Databricks Delta tables ensuring data accuracy completeness and consistency. The role requires strong SQL skills experience with Databricks and proficiency in defect management using JIRA.

Key Responsibilities

• Validate data in Databricks Delta tables across Bronze Silver and Gold layers.
• Perform source-to-target data validation and reconciliation.
• Verify data transformations business rules and aggregation logic.
• Execute SQL queries to validate data accuracy completeness consistency and integrity.
• Validate schemas data types null values duplicate records and primary key constraints.
• Perform regression integration and end-to-end testing for data pipelines.
• Create execute and maintain test cases and test scenarios based on business requirements.
• Log track and manage defects using JIRA and work closely with Data Engineers to resolve issues.
• Participate in Agile ceremonies including sprint planning daily stand-ups and retrospectives.
• Prepare test execution reports and communicate testing status to stakeholders.

Required Skills

• 3-5 years of experience in Data Testing ETL Testing or Data Validation.
• Hands-on experience with Databricks and Delta Lake.
• Strong proficiency in SQL for data validation and reconciliation.
• Experience validating ETL/ELT pipelines and data transformations.
• Knowledge of Spark SQL and Databricks notebooks.
• Experience with JIRA for defect tracking and test management.
• Understanding of data warehousing concepts and relational databases.
• Experience working in Agile/Scrum environments.
• Strong analytical troubleshooting and communication skills.

Preferred Skills

• Basic knowledge of PySpark and Python
• Experience with Azure Databricks.
• Experience with ETL testing.
• Exposure to Azure DevOps or Git for version control.
• Familiarity with data quality frameworks and test automation tools.

Core Validation Activities

• Source-to-target reconciliation
• Row count and record count validation
• Schema and data type validation
• Null duplicate and uniqueness checks
• Business rule validation
• Incremental and full load validation
• Delta table validation
• End-to-end data pipeline testing
• Defect logging and tracking using JIRA