Technical Data Analyst & Database Designer with a solid Computer Science background.
I specialize in the full data lifecycle: from SRS Analysis and EERD Modeling to Advanced Analytics and Secure BI Dashboards.

• Top 3 Winner – Marketing Analytics Hackathon (Orange Digital Center & Instant Software).
• Scholarship Recipient – Awarded BUE & AOU Merit Scholarships for academic excellence.
• Full-Stack Data Designer – Proven ability to translate complex business requirements (SRS) into optimized physical schemas.
• Software Engineer – Contributed to the development and optimization of AI models using advanced mathematical and programming solutions.And Applied problem-solving techniques to improve algorithm performance in AI-driven projects..

I work across the full data lifecycle — from understanding business requirements and designing relational databases, to analyzing data, building dashboards, and generating actionable insights.

• 📊 Data analysis, KPI monitoring & storytelling
• 🧠 Data Management, Data Abstraction and Data Warehousing Techniques
• 🗄 Database design, normalization & schema modeling
• 📈 Power BI dashboards with advanced DAX & RLS security & Endoresment levels
• 💡 Business-oriented analytical problem solving
• 🐍 Python for automation, EDA & machine learning
• 💻 Software Engineering & Applying CS fundamentals and robust algorithmic problem-solving techniques to ensure high-quality technical deliverables.

🔹 Database Architecture & Design

🔹 Advanced SQL & Query Optimization

🔹 Database Operations & Migration

🔹 Data Modeling & Architecture

🔹 ETL, Power Query & Data Shaping

🔹 Security, Governance & Deployment

🔹 Advanced DAX & Analytics

🔹 Performance & Optimization

🔹 Visualization & Business Intelligence

• Developed a marketing analytics solution to identify causes of declining conversion rates and low ROI.
• Restored and cleaned .bak database using SQL Server and Nested CTEs across 5 core tables: Customers, Products, Journey, Reviews, Engagement.
• Ensured 100% data accuracy by handling duplicates, nulls, and formatting issues.
• Engineered advanced KPIs in Power BI: Conversion Rate, Average Order Value (AOV), Customer Engagement Rate, ROI/CPA.
• Conducted sentiment analysis on customer reviews to identify pain points.
• Visualized the customer journey and provided actionable recommendations.
• Presented insights via live Power BI dashboards embedded in PowerPoint.

• Developed fraud detection system using Kaggle dataset (284,807 transactions, 31 features).
• Addressed class imbalance using NearMiss to create 50/50 Fraud vs Non-Fraud ratio.
• Selected and evaluated classifiers: Logistic Regression, Decision Trees, Random Forest, Neural Networks.
• Built Neural Network and compared performance against best classifier.
• Evaluated models with accuracy, precision, recall, and F1 score; achieved high reliability.
• Reduced potential financial losses by accurately distinguishing legitimate vs fraudulent transactions.
• Developed basic HTML/CSS/JS interface to demonstrate deployment.

• Designed and implemented enterprise-level Row-Level Security (RLS) for employees, managers, and admins.
• Built a custom security dimension (INFO SEC) to manage users, emails, territories, and hierarchy.
• Applied dynamic user-based filtering with USERPRINCIPALNAME() and managerial access logic.
• Developed multi-level hierarchical security using DAX-based hierarchy logic.
• Created role-based access models: Employee (self), Manager (team), Admin (full access).
• Ensured secure RLS propagation across fact and dimension tables; tested using Power BI Service.

• 📊 End-to-End Data Storytelling: Transforming raw datasets into compelling visual narratives that empower stakeholders to make data-driven decisions.

• 🏗️ Robust Data Architecture: Designing scalable database schemas and optimized ETL pipelines to ensure high data integrity and performance.

• 🧠 Advanced Analytical Logic: Leveraging Complex DAX and Statistical Python models to uncover hidden patterns and provide predictive insights.

• 🔐 Data Security & Governance: Implementing enterprise-level security models (like RLS) to protect sensitive information while maintaining accessibility.

• 💡 Business-Centric Problem Solving: Bridging the gap between technical complexity and business needs through proactive communication and strategic analysis.

• Advanced DAX Optimization
• Data Engineering Concepts
• ETL Pipelines
• Query Performance Tuning

"Good data tells you what happened. Great analysis tells you why."