Data Validation Engineer

·       Semantic Model Validation: ​Validate Power BI semantic models against Databricks Gold datasets. Verify: Relationships, Hierarchies, Measures, Dimensions, DAX calculations, ​Ensure KPI calculations align with approved business definitions.

·       Power BI Report Validation
​Validate report visuals, filters, slicers, drill-throughs, and aggregations.
​Compare dashboard results against approved business benchmarks.
​Verify report functionality across different user scenarios and security roles.
​Perform pre-demo and pre-release validation testing.
End-to-End Reconciliation Testing
Validate complete flow:

·       Source System
↓
Databricks Bronze
↓
Databricks Silver
↓
Databricks Gold
↓
Power BI Semantic Model
↓
Power BI Reports

·       Ensure metrics reconcile across every layer.

·       Defect Management
​Log, prioritize, and track reconciliation issues.
​Raise Jira stories and defects.
​Coordinate resolution with engineering and reporting teams.
​Validate fixes and perform regression testing.
UAT & Signoff Support
​Support business users during User Acceptance Testing.
​Document validation results and evidence.
​Provide readiness assessments for customer demos and production deployments.
​Maintain reconciliation and signoff documentation.

Job Responsibilities

·       Data Availability Assessment

·       Maintain inventory of all available datasets across brands and source systems.

·       Validate data availability, historical coverage, refresh frequency, and completeness.

·       Identify missing datasets and data gaps impacting reporting requirements.

·       Track source readiness for project onboarding and new report development.

·       Source-to-Databricks Reconciliation

·       Validate source system data against Databricks Bronze, Silver, and Gold layers.

·       Perform record count, aggregate, and metric-level reconciliation.

·       Validate business rules, mappings, transformations, and calculations.

·       Investigate discrepancies and document root causes.

·       Databricks Data Quality Validation: ​Validate ingestion pipelines and transformation logic. ​

·       Verify data quality metrics including: Completeness, Accuracy, Uniqueness, ​Consistency, ​Timeliness, Identify data loss, duplicates, mapping issues, and transformation defects.