Data Analyst | 5 years turning messy data into business decisions | Power BI · SQL · Python · Excel · GCP · Azure

I turn messy, high-volume data into decisions businesses act on. Five years building star-schema models on millions of records, SQL pipelines that got 40% faster, and ML models that flag risk at 87% precision. Microsoft Certified Fabric Analytics Engineer.

📍 Chicago, IL · 📫 thrineshvuribindi@gmail.com · 💼 LinkedIn

BI & Visualization

Data & Databases

Analytics

End-to-end claims analytics pipeline: root cause analysis across joined SQL tables, a cost-sensitive classification model flagging high-risk claims at 87% precision, and an operational KPI dashboard.

Python SQL Power BI DAX Power Query Excel

Microsoft Certified: Fabric Analytics Engineer Associate

Power BI is moving to Fabric, and I'm already there: lakehouses, data warehouses, semantic models, SQL analytics, and performance-tuned DAX on Microsoft's unified data platform. → Verify credential

• Built 10+ interactive Power BI reports with 40+ KPIs for performance monitoring and ad-hoc analysis
• Cut dashboard load times from 13s to 6s on 4M+ records with star-schema modeling and optimized DAX
• Automated Snowflake data quality checks across 10M+ records, reducing manual validation by 85%
• Developed predictive models for customer segmentation and claims risk scoring (K-means, RFM, classification)
• Uncovered a 26% retention decline among first-quarter signup customers by analyzing 500K+ records, driving retention of 10% of at-risk accounts
• Modeled pricing and profitability in Excel across 100K+ transactions, improving margin accuracy by 8%