I led the productization of Vista Analytics — a consolidated analytics capability for internal ops and external customers. We designed ETL pipelines to centralize operational and leasing data into Redshift, created reusable data models (Glue), and delivered dashboards through QuickSight and Power BI connectors. Beyond reporting, we introduced AI/ML models to generate recommendations for agents and highlight churn risks. The platform shortened QBR preparation time, improved decision-making with real-time KPIs, and enabled customers to self-serve insights previously hidden and costly to extract.
Highlights
- Defined analytics roadmap and prioritized KPIs.
- Oversaw ETL design (Glue) & data modeling.
- Delivered BI via QuickSight & Power BI.
- Introduced AI features for anomalies & actions.
- Built access controls & tenant-aware reports.
- Drove adoption through templates & training.
Impact
- Reduced QBR prep time (hours → minutes).
- Increased BI adoption (active users).
- Automated insights consumed by teams.
- Achieved performance/refresh SLAs.
- Enabled customer self-serve analytics.
Tech / Architecture
- AWS Redshift (Warehouse)
- AWS Glue (ETL)
- QuickSight (BI)
- Power BI Connectors
- SageMaker (ML Models)
- ETL Orchestration (Step Functions)
Key Artifacts
ETL Flow Diagram
Diagram showing the data flow from source to dashboard.
Executive KPI Dashboard
Screenshot of the internal KPI dashboard for leadership.
Example AI Insight
A mock-up of an automated insight/alert for agents.