Hello, I'm Mohamed Saied
Designing Scalable Data Platforms & Lakehouse Architectures for Enterprise Systems
I'm a Senior Data Engineer & Architect with 19+ years of experience designing and delivering enterprise-grade data platforms, ETL pipelines, and analytics solutions.
I specialize in transforming complex data into scalable, reliable, and high-performance systems that empower business decisions and digital transformation initiatives.
My work spans across data warehousing, data lakehouse architectures, cloud platforms, and real-time data processing — helping organizations unlock the true value of their data.
What I Do
- Design scalable Data Platforms & Lakehouse architectures
- Build and optimize ETL/ELT pipelines at scale
- Lead data architecture and cloud transformation initiatives
- Enable analytics, BI, and data-driven decision making
Education
PhD Candidate in Data Science / Advanced Analytics
Cairo University • 2025 – Present
Research focused on advanced analytics, machine learning, and real-world data applications.
Master’s Degree in Data Science & Cloud Computing
Cairo University • 2022 – 2025
Focused on cloud-native data platforms, machine learning, and scalable analytics systems.
Diploma in Big Data & Machine Learning
Nile University • 2018 – 2020
Bachelor’s Degree in Communications & Electronics Engineering
Alexandria University • 2001 – 2006
Featured Projects
Enterprise Data Lakehouse Platform
Designed and implemented a scalable data platform integrating data lake and data warehouse concepts, enabling real-time analytics and self-service BI capabilities across the organization.
Airflow-based Data Pipeline Orchestration
Built and orchestrated end-to-end ETL pipelines using Apache Airflow, improving data reliability, optimizing scheduling, and reducing processing time across multiple data sources.
Anomaly Detection System
Developed machine learning models to detect anomalies in water and electricity datasets, enabling early detection of issues and supporting data-driven operational decisions.
Skills & Technologies
Data Engineering
- • ETL / ELT Pipelines
- • Data Modeling (DWH, Star Schema)
- • Data Integration
- • Batch & Real-time Processing
Big Data & Tools
- • Apache Airflow
- • Spark, Hadoop Ecosystem
- • Kafka (Streaming)
- • dbt
Cloud Platforms
- • AWS (S3, Redshift, Glue, Lambda)
- • Azure Data Services
- • Cloud Architecture
- • Infrastructure as Code
Architecture
- • Data Lakehouse Architecture
- • Medallion, Lambda & Kappa Architecture
- • Microservices & Event-driven Systems
- • Data Governance (NDMO aligned)
- • CDMP & TOGAF
- • Scalable System Design
Data Science & AI
- • Machine Learning (Supervised & Unsupervised)
- • Statistical Analysis & Data Modeling
- • Python (Pandas, NumPy, Scikit-learn)
- • Anomaly Detection & Predictive Analytics
BI & Analytics
- • Power BI
- • Data Visualization
- • Self-Service BI
- • Business Insights
Databases
- • SQL Server, Oracle
- • PostgreSQL
- • Data Warehouses
- • Performance Tuning
Architecture
Real-world data architectures I design and implement to enable scalable, reliable, and high-performance data platforms.
Enterprise Data Lakehouse
Designed scalable data platforms combining Data Lake and Data Warehouse capabilities, enabling batch and real-time processing, centralized storage, and self-service analytics.
ETL Orchestration with Apache Airflow
Built and orchestrated end-to-end ETL pipelines using Apache Airflow, enabling scheduling, monitoring, retry handling, and dependency management across complex workflows.
Real-Time Streaming Platform
Designed event-driven architectures using streaming technologies to enable real-time data processing, low-latency analytics, and scalable data ingestion pipelines.
Machine Learning & Anomaly Detection
Developed end-to-end machine learning pipelines for anomaly detection and predictive analytics, integrating data preprocessing, model training, deployment, and monitoring into production systems.
Let’s build something impactful with data
Explore my insights, projects, and real-world experience
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