Hi there, I'm Karthik Beesa 👋


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🚀 About Me

I'm a highly analytical and results-driven Data Analyst with 5+ years of experience managing complex data pipelines, designing comprehensive BI dashboards, and executing rigorous data validation workflows. I specialize in transforming raw corporate data into scalable, automated, and audit-ready reporting ecosystems that directly support strategic business decisions.

Beyond data architecture and deep SQL querying, I'm passionate about sharing technical insights, automation workflows, and data strategy guides through platforms like YouTube, Medium, and Instagram to help upskilling analysts master modern reporting tools.

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✨ Highlights

| | |
|---|---|
| 📊 Dashboard Architect | Own and maintain 12+ enterprise Power BI, Tableau & Excel dashboards supporting cross-functional financial operations |
| ⚙️ Efficiency Engineer | Built SQL & Python scripts that reclaim 5–6 hours of manual execution time per week |
| 🎓 Academic Excellence | M.S. in Technology Management — Lindsey Wilson College, GPA: 3.88 |
| 🍛 Late-Night Engineer | Fuelled by large trays of biryani while debugging SQL queries at midnight |

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🛠️ Technical Stack

!SQL Server
!Python
!Power BI
!Tableau
!Excel
!AWS
!Azure
!Pandas
!NumPy
!R

| Category | Technologies & Tools |
| :--- | :--- |
| SQL & Querying | SQL Server, Joins, CTEs, Window Functions, Stored Procedures, Query Refactoring |
| BI & Visualization | Power BI (DAX, Power Query), Tableau, Advanced Excel (PivotTables, Index/Match) |
| Programming | Python (Pandas, NumPy), R (Basic Statistical Analysis) |
| Data Quality & Cloud | AWS (S3, Lambda, CloudWatch), Azure, Source-to-Report Reconciliation, Regression Testing |
| Analytics & Reporting | KPI Scorecards, Operational Dashboards, Trend & Variance Analysis, Segmentation |

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📈 Featured Projects

🤖 AI-Powered Business Analytics Tool

> Built a modular Python pipeline (Pandas + NumPy) to clean and process 10,000+ records, cutting dataset preparation time by 60%. Designed for scalable, repeatable analytics workflows. > > ### ☁️ Cloud-Based Inventory Reporting (AWS) > > Constructed an automated inventory ingestion pipeline on AWS with 8 validation checkpoints and CloudWatch monitoring to capture anomalies in real time — zero manual intervention required. > > > > ### 🗃️ SQL Financial Data Reconciliation Framework > > > Engineered a modular framework of 20+ validation queries that compressed manual reconciliation cycles from 2.5 hours down to under 30 minutes — an 80% time reduction. > > > > > > --- > > > > > > ## 📊 GitHub Stats > > > > > >

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🛠️ Conceptually driven. Data validated. Let's build something insightful.


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