Databricks Tester

Stefanini; Mumbai, Bengaluru; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Stefanini in Mumbai seeks a Databricks Tester with 3-5 years of data testing and validation experience. The role focuses on validating Databricks Delta tables, data pipelines, and complex transformations to ensure accuracy and integrity.

You will perform source-to-target validation, write SQL queries, and log defects in JIRA while working within an Agile team to deliver quality data solutions.

Qualifications

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

Responsibilities

• Validate data in Databricks Delta tables across Bronze, Silver, and Gold layers.
• Perform source-to-target data validation and reconciliation.
• Verify data transformations and business rules.
• Execute SQL queries to ensure data accuracy and integrity.
• Check schemas, data types, nulls, duplicates, and primary keys.
• Conduct regression, integration, and end-to-end testing for data pipelines.
• Create, execute, and maintain test cases from business requirements.
• Log and track defects in JIRA and collaborate with Data Engineers.
• Participate in Agile ceremonies and sprint rituals.
• Prepare test execution reports and communicate status to stakeholders.

Skills

Data Testing

SQL proficiency

Agile/Scrum

Analytical thinking

Communication skills

Tools

Databricks

Delta Lake

JIRA

Azure DevOps

Git

Job description

Hello All,

We have below mentioned position with us for the client @ Mumbai.

Shift timings

• 8:30 AM 4:30 PM
• 12:30 PM - 8:30 PM

Key Responsibilities Job SummaryWe 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 Databricksand Delta Lake.
• Strong proficiency in SQLfor data validation and reconciliation.
• Experience validating ETL/ELT pipelines and data transformations.
• Knowledge of Spark SQLand Databricks notebooks.
• Experience with JIRAfor 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 PySparkand 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