Data Analyst and Data Engineer with 9 years of domain expertise in international trade, logistics, and supply chain operations. I build end-to-end analytical systems — from raw operational data to executive dashboards — using SQL Server, Python, and Power BI.
My work lives at the intersection of data engineering and logistics analytics: medallion-architecture warehouses, predictive delay models, and KPI frameworks that turn fleet, freight, and customs data into operational decisions.
| Category | Technologies |
|---|---|
| Databases |  SQL Server  T-SQL |
| Languages |  Python  TypeScript  HTML  CSS |
| Data & ML |  Pandas  NumPy  scikit-learn XGBoost  Matplotlib |
| BI & Viz |   Jupyter  |
| Architecture | Medallion (Bronze → Silver → Gold) Star Schema ETL/ELT Data Quality |
| Tools |  Git  Docker  Playwright  Next.js |
Production-grade data warehouse for a 120-truck, 85,000-load/year logistics operation.
549,706 rows → Bronze → Silver → Gold → 11 business views → $298.6M revenue explained
Complete medallion pipeline on SQL Server 2025: 14 operational tables transformed into a conformed star schema with surrogate keys, enforced referential integrity, and a management-queryable semantic layer. Every KPI cross-validated against source data — 0 rows dropped, 0 orphaned references.
T-SQL SQL Server Medallion Star Schema ETL Data Quality KPI Framework
→ Repository | → Documentation
Full medallion-architecture warehouse integrating CRM and ERP source systems.
Bronze → Silver → Gold pipeline built entirely in T-SQL with stored procedures, surrogate keys via ROW_NUMBER(), MDM business rules with CRM/ERP fallback logic, and comprehensive data quality tests. Draw.io architecture diagrams document every layer.
T-SQL Data Warehousing Stored Procedures MDM Data Modeling draw.io
Maritime AIS data analysis — 10.6 million vessel tracks across six US regions in 2025.
GeoPackage spatial database profiled with SQL and Python: fleet composition (45% recreational, 26% commercial), 2.6× seasonal amplitude (August peak vs December trough), regional traffic distribution, and vessel-type behavior patterns. Full case study with SQL queries, EDA charts, and maritime planning recommendations.
Python SQL GeoPackage Geospatial Maritime AIS Matplotlib
Predictive modeling of supply chain delays with 84.2% accuracy.
Polyglot analysis across Excel, SQL Server, and Python/Jupyter. Feature-engineered 15 operational variables, trained XGBoost/Random Forest/Gradient Boosting classifiers, identified traffic and waiting time as top delay predictors. Packaged with an interactive Excel dashboard and a standalone SQL analysis script.
Python XGBoost scikit-learn Pandas SQL Server Power BI Jupyter
Medallion-architecture analysis of GHG emissions across 1,016 industries.
End-to-end pipeline: raw CSV → Bronze → Silver → Gold views on SQL Server → Python EDA with PyODBC → Power BI dashboard. Right-skewed distribution analysis, outlier detection, industry ranking with actionable sustainability recommendations.
Python SQL Server Medallion PyODBC Power BI Matplotlib Seaborn
Realistic ocean freight data generator for data engineering workloads.
862-line Python generator producing relational shipping datasets — ports, vessels, shipments, containers, bills of lading, tracking events — with 95% clean / 5% anomalous records, weighted trade lanes, and peak-season simulation. Outputs Parquet or CSV at scale (tested to 100M+ shipments).
Python Pandas NumPy Parquet Data Generation Multiprocessing
Playwright-powered web scraping toolkit for customs tariff and legislation data.
Modular scraper architecture extracting HS code classifications from public Egyptian Customs pages with legal disclaimers, sample output, and test suite.
Python Playwright Web Scraping Customs Data Engineering
- Data Warehousing — designing medallion-architecture warehouses on SQL Server with enforced data quality and audit trails
- Logistics Analytics — building KPI frameworks, delay prediction models, and operational dashboards for fleet, freight, and supply chain
- Pipeline Engineering — idempotent ETL/ELT pipelines with
TRY_CONVERT-safe casting, orphan handling, and row-level auditability
I spent nearly a decade inside international trade operations — customs clearance, fleet coordination, exception resolution, freight forwarding — before pivoting into data. That domain fluency is my edge: I do not just model logistics data; I understand what a detention hotspot costs, why 44.6% on-time delivery for three straight years signals a structural problem, and which questions management will ask before they ask them.
Every project in this profile combines both halves: the operational reality of moving goods across borders and the engineering discipline of production-grade data systems.
Built for recruiters hiring Data Analysts, Data Engineers, and Logistics Analytics specialists.