Job Description
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 solid 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.
Solid 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.
Robust 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

📌 Databricks Tester Data Validation Mumbai
🏢 Stefanini Group
📍 Mumbai