I create structured data pipelines for businesses, enabling smarter, faster decisions. My technical expertise, problem-solving skills, and hands-on experience ensure scalable, reliable data infrastructure, bridging data complexity with business clarity.
I’m Sarah Mohamed, a motivated data engineering enthusiast passionate about transforming raw, messy data into clean, structured, and actionable pipelines that empower businesses to make smarter, faster, and data-driven decisions. With hands-on experience in ETL workflows, databases, and cloud tools, I focus on building scalable and reliable data infrastructures that support growth and innovation. My strength lies in bridging the gap between data complexity and business clarity — delivering not just pipelines, but real business value.
I combine technical expertise, problem-solving skills, and collaboration to design solutions that streamline data flow, optimize performance, and unlock meaningful insights. I’m always open to collaborating on projects, internships, and opportunities where I can apply and expand my skills in data engineering and analytics.
Turning results into clear, actionable insights.
Professional-grade data preprocessing that fuels smarter machine learning
Uncovering insights through visualizations and statistical analysis to identify patterns, correlations, and business opportunities .
Created a machine learning model to predict telecom customer churn using a dataset of demographics, service usage, billing, and contract details. Key drivers of churn were identified through exploratory data analysis. Multiple algorithms were compared and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Random Forest and Logistic Regression were found to provide the best balance between performance and interpretability.
View ProjectAnalyzed the Titanic dataset to explore survival patterns across demographics, passenger class, and fares. Using Matplotlib and Seaborn, I created bar plots, histograms, box plots, scatter plots, and heatmaps, customizing them with labels, colors, and annotations to highlight insights such as higher survival among women, children, and first-class passengers.
View ProjectDesigned a Star Schema data model for a coffee shop sales dataset, creating a central fact table for transactions and dimension tables for date, product, and payment details. This reduced redundancy, improved query performance, and enabled insights into sales trends, peak hours, and payment preferences.
View Projectsarah7831@gmail.com
+201128867665
Egypt
Helwan University, 2023-2027
Maadi STEM School for Girls, 2020-2023
Digital Egypt Pioneers Initiative - DEPI, 2025-Present
