- 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