I build data pipelines and practical data products with Python and SQL. I enjoy working across the full path from ingestion and validation to transformation, orchestration, and analytics. I also work in technical education, where I turn complex ideas into hands-on learning experiences.

My current focus is becoming stronger at production-style data engineering: reliable ingestion, incremental processing, data quality, observability, and clear documentation.

Project	Focus
Pharmacy Stock Monitoring RAG	Kafka events, validation, alerts, forecasting, and operational monitoring.
PharmStock AI Data Platform	Spark, Kafka, Airflow, dbt, MLflow, and Streamlit.
NYC Taxi Lakehouse	Bronze/Silver/Gold pipeline, quality checks, incremental loading, and backfills.
Fabric Retail Lakehouse	OneLake-oriented medallion design, Spark notebooks, pipeline orchestration, SQL serving, and DP-700 preparation.
Sales Analytics Query Optimization	SQL modeling and query performance work.

I start with the data contract and the business question. Then I make the pipeline reproducible, separate raw data from curated models, add checks for both schema and business rules, and document the decisions that matter. A pipeline is not finished when it runs once; it should also be understandable, testable, and safe to rerun.

• Clear communication with technical and non-technical stakeholders
• Breaking ambiguous business questions into practical data tasks
• Ownership of work from planning and implementation to documentation
• Troubleshooting calmly and working through failures systematically
• Collaborative code reviews and constructive feedback
• Technical mentoring and simplifying complex concepts for learners
• Time management, prioritization, and consistent follow-through

• Bachelor’s Degree in Computer Science, Faculty of Computers and Information, Minia University
• Microsoft Certified: Azure Data Fundamentals (DP-900)
• Microsoft Fabric Data Engineer Associate — DP-700 Candidate / Certification in Progress