Resume | Jamal El-Shenawy

Professional Summary

Data Engineer with 2+ years of experience designing and implementing scalable ETL pipelines, data workflows, and analytics solutions. Proficient in Python, SQL, and modern data engineering tools. Strong background in machine learning, web development, and data analysis. Proven track record of building robust data infrastructure and delivering data-driven solutions.

Technical Skills

Programming

Python, JavaScript, SQL, HTML/CSS

Data Engineering

Apache Airflow, Apache Spark, ETL Pipelines, Data Warehousing

Databases

PostgreSQL, MySQL, MongoDB, SQLite

Data Science & ML

Pandas, NumPy, Scikit-learn, TensorFlow, Keras

Web Scraping

BeautifulSoup, Scrapy, Selenium, Playwright

Web Development

React, Node.js, Django, Flask, FastAPI

Education

Bachelor of Science in Computer Science and IT

Egypt Japan University of Science and Technology (E-JUST)

Alexandria, Egypt

Active in AI/ML research and full-stack development projects
3rd place in robotics competition
Engaged in academic societies focused on AI, data science, and competitive technology projects

Professional Experience

Data Engineer

Freelance

2023 - Present

Design and implement ETL pipelines for data extraction, transformation, and loading
Develop web scraping solutions to collect and structure data from various sources
Build API integrations for seamless data flow between systems
Optimize data workflows for improved performance and reliability

Full-Stack Developer

Various Projects

2022 - Present

Developed responsive web applications using modern JavaScript frameworks
Implemented backend services with Python (Django/Flask) and Node.js
Designed and optimized database schemas for efficient data storage
Ensured code quality through testing and version control best practices

Key Projects

CineVerse - Netflix-Style Streaming Platform

HTML, CSS, JavaScript

Built responsive streaming platform with dynamic content loading
Implemented intuitive UI/UX with smooth animations
Optimized performance through efficient DOM manipulation

Credit Scoring Machine Learning Model

Python, Pandas, Scikit-learn

Developed ML model for credit risk assessment
Performed feature engineering and model evaluation
Achieved high accuracy through cross-validation techniques

MNIST Handwritten Character Recognition

Python, TensorFlow, Keras

Implemented neural network for digit classification
Applied data preprocessing and augmentation techniques
Optimized model performance through hyperparameter tuning

Languages

Arabic (Native) English (Advanced) German (Basic) Japanese (Beginner)