E-commerce Data Warehouse

Problem

E-commerce businesses need reliable, scalable data infrastructure to support analytics and business intelligence. Raw transactional data from multiple systems (CRM, ERP) requires cleaning, deduplication, and transformation into analytics-ready formats with guaranteed data quality and complete audit trails.

Constraints

• Multiple data sources with varying schemas and quality
• Significant data duplication requiring intelligent deduplication strategies
• Need for NULL-safe transformations and robust surrogate key generation
• Production-grade CI/CD requirements with secure authentication
• Complete audit trail for governance and compliance
• Zero data loss tolerance for financial data

Architecture

Built a cloud-native medallion architecture following modern data engineering best practices:

CSV Sources → GCS → BigQuery Bronze → dbt Transform → Silver/Gold → BI Analytics

Data Flow

1. Bronze Layer: Raw CSV ingestion to BigQuery (6 source tables, 116K+ records)
2. Silver Layer: Cleaned and deduplicated data with quality validations
3. Gold Layer: Star schema optimized for analytics with incremental MERGE
4. QA Layer: Complete audit trail tracking all data quality issues

Results & Impact

Data Processing Success

• 116,294 → 79,153 records: 32% intelligent deduplication across all layers
• Zero Data Loss: Complete audit trail with 17,996 QA records
• 100% Critical Coverage: 18/19 comprehensive tests passing
• Production Ready: 8-second end-to-end pipeline execution

Technical Excellence

• NULL-Safe Processing: 100% valid surrogate key generation
• Smart Deduplication: Priority-based customer record consolidation
• Complete Validation: Automated testing preventing data quality issues
• Enterprise CI/CD: GitHub Actions with Workload Identity Federation