Building scalable ETL/ELT pipelines, cloud data architectures on Azure & Big Data solutions with Apache Spark and Airflow — turning raw data into reliable, decision-ready systems.
I'm Mennatullah Mohammed, a passionate Data Engineer and MIS student at Capital University in Cairo, Egypt. I specialize in building robust, scalable data architectures that power business intelligence at scale.
With hands-on experience in Apache Airflow, Azure Synapse, Apache Spark and modern data modeling, I design end-to-end ETL/ELT systems that are reliable, efficient and cloud-native.
Beyond engineering, my analytics background — from Power BI dashboards to statistical analysis — lets me bridge the gap between raw data and actionable business insights. I believe great data engineering is invisible: it just works.
Technologies I use to engineer data pipelines, build cloud architectures, and extract value from data.
I love architecting end-to-end pipelines that transform messy raw data into clean, reliable datasets decision-makers can trust instantly.
Crafting structured, insight-rich data models is where engineering truly becomes business value — I bridge the technical and the strategic.
I plan, prioritize, and ship — delivering scalable cloud solutions on time without compromising on data quality or architecture integrity.
Every broken pipeline or messy dataset is a puzzle. I enjoy debugging and optimizing until every byte flows perfectly through the system.
I thrive in cross-functional teams where data, engineering, and business decisions move together toward a shared goal.
A selection of data engineering and analytics work.
Continuous learning across data engineering, cloud, analytics and professional skills.
Open to opportunities, collaborations and a good data conversation.
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