I build reliable ETL pipelines, data warehouses, and analytics workflows using SQL, Python, and Azure.
I'm Mohamed Abdellatef, a Junior Data Engineer focused on turning raw operational data into clean, trusted, and analytics-ready datasets.
My recent work includes a fintech lakehouse pipeline built with ADF, ADLS, Databricks, dbt, and Power BI, plus a Kafka-based real-time pipeline for streaming ingestion and transformation. I focus on readable SQL, clean Python, and datasets that stay understandable as they scale.
Built a layered warehouse with staging, transformation, dimensional modeling, and analytics-ready tables for structured reporting.
Designed a cloud-based ingestion and transformation flow using Azure services to move raw source data into structured reporting layers.
Prepared cleaned datasets and reporting structures for dashboard consumption with a focus on consistency and trusted business outputs.
Focused around Azure data platforms, SQL transformation, Python ETL, orchestration, and streaming systems.
A mix of structured training and practical delivery experience across software, AI, and technical problem solving.
Built practical engineering discipline through structured project work, collaboration, and hands-on implementation in a professional training environment.
Worked in an AI-focused environment that strengthened analytical thinking, data quality awareness, and the ability to evaluate outputs with precision.
My degree built the base for database systems, programming, software engineering, and the technical mindset behind data engineering work.
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Egyptian E-Learning University (EELU)