- Data Engineers, BI Developers, Product Owners, Solution Architects to validate end-to-end data pipelines, data transformations, reporting solutions, and analytics applications.

Responsibilities : -

- Drive quality improvements through automation and process optimization.

- Collaborate with architecture, engineering, and business teams to define acceptance criteria and quality metrics.

Databricks Testing : -

- Validate data ingestion, transformation, and processing workflows built in Databricks.

- Test PySpark notebooks, Delta Lake implementations, and ETL/ELT pipelines.

- Validate Medallion Architecture layers (Bronze, Silver, Gold).

- Test data integrity across source and target systems.

- Perform reconciliation testing between upstream and downstream datasets.

- Conduct schema validation and data quality testing.

Power BI Testing : -

- Validate Power BI dashboards, reports,



and semantic models.

- Test calculated columns, DAX measures, KPIs, and business metrics.

- Perform report regression testing after model changes.

- Validate row-level security (RLS) implementation.

- Verify drill-through functionality and report interactions.

- Ensure consistency between source data and visualized metrics.

Test Automation : -

- Develop comprehensive data quality test plans.

- Execute source-to-target reconciliation testing.

- Validate business rules and transformation logic.

- Identify root causes of data quality issues and work with engineering teams on remediation.

- Perform user acceptance support and release validation.

📌 QA Engineer (Pune)
🏢 Contactx Resource Management
📍 Pune