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Turning messy data into trusted business insights
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Turning messy data into trusted business insights

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thrinesh13/README.md

Thrinesh Vuribindi, Data Analyst and BI Developer

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Data Analyst and BI Developer with 4.5+ years of experience in healthcare, finance, and operations analytics.
I build end-to-end BI solutions, from SQL and Python data preparation to star-schema semantic models, DAX measures, and Power BI dashboards.

💼 What I Do

  • BI development: Power BI reports and semantic models, DAX measures, Power Query, drill-through and what-if analysis
  • Data modeling and preparation: star schemas, SQL transformations, medallion lakehouse pipelines in Microsoft Fabric
  • Analytics: KPI design, data validation and reconciliation, statistical testing, trend analysis

📊 Featured Projects

Project What I built and found Tools Code
Surgical Scheduling Variance Analysis
Healthcare operations		 Built a Fabric medallion pipeline and Power BI semantic model with 17 DAX measures on 48,118 surgical cases. Ranked the 133 procedures that drive 53.6% of scheduling variance Microsoft Fabric, PySpark, Spark SQL, Power BI, DAX Repo
Medicare Claims Payment Integrity Analytics
Healthcare payment integrity		 Built staging, core, and mart layers in PostgreSQL and a 4-page star-schema Power BI report. 10 SQL prepayment edits caught 82% of improper claims and cut modeled review cost from $425K to $120K SQL, PostgreSQL, Python, Power BI, DAX, Power Query Repo
Statistical Time-Series Analysis
Financial markets		 Tested 4 market assumptions on 1,254 trading days with t-tests, correlation, and normality tests. 3 were not supported by the data Python, pandas, SciPy, Matplotlib Repo

🏅 Certification

Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)

🛠️ Tech Stack

Area Tools
BI and visualization Power BI DAX Power Query Tableau Excel
Data platforms and modeling SQL Microsoft Fabric Snowflake Databricks dbt PostgreSQL
Analytics and programming Python pandas PySpark SAS Git

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  1. Medicare-Claims-Payment-Integrity-Analytics Medicare-Claims-Payment-Integrity-Analytics Public

    Medicare Part B payment integrity analysis: 10 SQL prepayment edits route 96% of claims without manual review, cutting decision cost 72% ($425K to $120K). Python, PostgreSQL, Power BI.

    Jupyter Notebook 1

  2. Surgical-Scheduling-Variance-Analysis Surgical-Scheduling-Variance-Analysis Public

    End-to-end Microsoft Fabric: 48,118 cases across 418 procedures to identify operating room scheduling variance, $15.8M–$27.1M in an estimated annual cost exposure, 133 procedures driving 53.6%.

    HTML 1

  3. Statistical-Time-series-analysis Statistical-Time-series-analysis Public

    Statistical data analysis to analyze five years of time-series data, applying hypothesis testing, correlation analysis, trend analysis, and data visualization to identify patterns and relationships.

    Jupyter Notebook