Project Overview:
Led data warehouse modernization initiatives for Chewy's Learning and Development department, focusing on consolidating disparate learning management systems into a unified analytics platform. This transformation enabled data-driven decision making across the organization while optimizing costs and improving data accessibility.
Key Responsibilities:
- Architected and implemented enterprise data models in Snowflake
- Developed automated ETL processes for multiple data sources
- Established data governance frameworks
- Maintained comprehensive technical documentation
Technical Environment:
- Data Warehouse: Snowflake
- ETL Tools: Python
- Visualization: Tableau
- Source Systems: Learning Management Systems
Major Achievements:
- Learning Analytics Platform Consolidation
- Challenge: Multiple Learning Management Systems operating in silos created data inconsistencies, reporting inefficiencies, and high maintenance costs. Stakeholders struggled to get a unified view of learning metrics across the organization.
- Solution & Impact: Designed and implemented a centralized Snowflake data platform:
- Unified data from multiple LMS platforms
- Standardized metrics across systems
- Decreased report generation time from days to hours
- Learning Systems Consolidation
- Challenge: Three separate Learning Management Systems with disparate data structures, inconsistent metadata, and varying business rules. Business teams struggled with data standardization and reporting across platforms.
- Solution & Impact: Led technical implementation of LMS consolidation:
- Collaborated with business teams to standardize learning metrics
- Mapped data elements across all three systems
- Planned data validation and transformation rules
- Brainstormed automated data quality checks
- Successfully migrated historical data with high accuracy
- Reduced reporting complexity for business users
- Learning Analytics Governance Framework
- Challenge:Lack of structured approach to data governance was creating risks around compliance reporting and data usage. No clear roadmap existed for scaling analytics initiatives.
- Solution & Impact: Developed comprehensive analytics and governance strategy:
- Created data governance framework for learning data
- Established metadata management practices
- Defined data quality standards and monitoring
- Built analytics roadmap aligned with business objectives
Skills Advanced:
- Data Warehouse Architecture
- ETL Pipeline Development
- Data Modeling
- Python Programming
- Tableau Dashboard Design
- Data Governance
