THE THROUGH-LINE
I have worked across data engineering, migration, analytics, machine learning, AI, software and commercial operations. The through-line is translating messy constraints into reliable data products, analysis and systems people can actually trust and use.
Network International
01Career
The chronology matters, but the useful signal is how the work moved from technical execution toward larger data, product and business decisions.
Network International
Enterprise data engineering, integration and migration for banking clients across Africa and the GCC, with hands-on ETL, source-to-target mapping, transformation, profiling, cleansing, validation and reconciliation.
Client-identifying and proprietary implementation detail remains intentionally limited.Al Tayseer International
Consolidated multi-brand and external-agency performance data across four group companies into a unified reporting view, aligning KPIs, timelines and deliverables for monthly commercial decision-making.
Guksu
Built recurring sales, inventory, production and warehouse reporting, then aligned manufacturing and sales plans with raw-material and finished-goods availability.
Egyptian African Trade
Extracted ERPNext sales, inventory and warehouse data, defined KPI and reporting logic with operations and finance, and connected paid-social performance data with commercial outcomes.
02Alongside the main lane
Teaching, independent delivery and early technical work explain the range without competing with the main career chronology.
Orcas Online
Tutor Python, Java, data analysis, data science and machine learning, adapting explanations and exercises to different levels and goals.
Self-employed
Delivered two business websites end-to-end, from requirements and structure through coded implementation, launch, hosting, domains and analytics.
National Authority for Remote Sensing & Space Sciences (NARSS)
Built remote-sensing data-analysis systems using machine learning and deep learning on cloud infrastructure.
Pharaonic Petroleum Company (PhPC)
Designed and implemented IT solutions during a technical internship.
Zewail City for Science & Technology
Implemented machine-learning, deep-learning and reinforcement-learning projects.
03Learning
Education and credentials stay visible because they explain the range. They are evidence, not decoration.
Credentials
Databricks
Udemy · Ed Donner
McKinsey Academy
Coach Academy
Canadian International College
Education
Canadian International College
CGPA 3.55ExploreAI Academy / ALX / African Leadership University
15-month scholarshipManarat Jeddah International Schools · Egypt & Jeddah, Saudi Arabia
04Operating stack
No proficiency meters and no logo wall. The useful signal is the combination of domains and the way the tools fit together.
Advanced SQL · ETL & Data Pipelines · Data Migration · Source-to-Target Mapping · Data Transformation · Data Profiling · Data Cleansing & Validation · Data Reconciliation · PostgreSQL
Databricks · AWS Foundations · Supabase · ERPNext
Python · pandas · NumPy · scikit-learn · SQL · TypeScript / JavaScript (basic)
Power BI · Advanced Excel · KPI Design · Dashboards · Reporting & Analytics
Deep Learning · TensorFlow · PyTorch · NLP · Generative AI & LLMs · RAG · Agents · Statistical & Probabilistic Modeling
Requirements Gathering · Stakeholder Management · Cross-functional Collaboration · Problem Solving · Team Leadership
05How I work
Start by understanding what is known, what is missing and what should not be claimed. Good systems become easier to build once the evidence boundary is explicit.
Turn unclear requirements, fragmented data and competing constraints into a shared model that technical and business stakeholders can reason about together.
Use the simplest architecture that can carry the real job, then add complexity only when the evidence or operating constraints require it.
