Designing dependable data platforms — from ingestion and transformation to governance, analytics, and AI-ready data.
I’m a data professional focused on building trustworthy, analytics-ready data systems. My work and learning span data engineering, data management and governance, data quality, dimensional modeling, business intelligence, and applied AI.
I enjoy connecting the full data lifecycle: integrating data from multiple sources, organizing it into reliable lakehouse and warehouse layers, applying quality and governance practices, and making the resulting data useful for reporting and intelligent applications.
- Data engineering: ETL/ELT, orchestration, lakehouse architecture, incremental loading, and distributed processing.
- Data management & governance: data quality, profiling, validation, reconciliation, standardization, and DAMA-DMBOK / CDMP-aligned concepts.
- Analytics: dimensional modeling, star schemas, SQL, dashboards, and business-focused metrics.
- Applied AI: hands-on work with Retrieval-Augmented Generation (RAG) systems.
- Knowledge sharing: Data Engineering Instructor and Graduation Project Mentor at the Information Technology Institute (ITI).
- Education: Master’s in Data Science at Cairo University; B.Sc. in Computer and Control Engineering.
Core practices: ETL/ELT · Pipeline orchestration · Medallion architecture · Incremental loading / CDC concepts · Lakehouse · Data warehousing · Distributed processing |
Core practices: Data profiling · Validation and reconciliation · Standardization · Data quality rules · Business definitions · Ownership concepts · Governance principles CDMP is referenced here as a framework of study and practice, not as a claim of certification. |
Core practices: Star schema · Fact and dimension tables · Data warehouse design · KPI reporting · Data exploration · Data validation |
Focus: Hands-on RAG system development · Data preparation for AI applications · Python-based workflows · Version control and reproducible projects |
Microsoft Fabric · Data Factory Pipelines · Lakehouse · PySpark · Delta Lake · Warehouse · Power BI
- Built a parameterized ingestion workflow using HTTP, Lookup, ForEach, and Copy activities to ingest eight datasets from GitHub into a Lakehouse Bronze layer.
- Configured daily orchestration and pipeline-status notifications through Outlook and Microsoft Teams.
- Applied cleansing and validation in the Silver layer using PySpark and Delta Lake, including deduplication, null handling, and key checks.
- Implemented incremental loading using a watermark / CDC-style approach into a Warehouse.
- Designed a Gold-layer star schema and connected Power BI for sales analytics, with reported project metrics of 18.5M in sales and 7.9M in profit.
Applied AI · Retrieval · Context-grounded generation
- Hands-on experience contributing to the development of a RAG system for an AI-powered application.
- Interested in the data foundations that make retrieval-based systems more useful: preparing source data, structuring content, and supporting relevant context retrieval.
AWS IoT Core · Lambda · Redshift · CloudWatch · OpenSearch
- Built a real-time ingestion and processing workflow for sensor data and weather API inputs.
- Used Amazon Redshift as a centralized warehouse for historical analysis and time-series data.
- Applied monitoring and observability practices using CloudWatch and OpenSearch.
SQL Server · ETL · Dimensional Modeling · Data Profiling
- Designed a retail warehouse using conceptual, logical, and physical data modeling principles.
- Built a star schema with fact and dimension tables for sales reporting.
- Integrated more than 100,000 CRM and ERP records into a centralized SQL Server warehouse.
- Created checks for duplicates, missing values, inconsistent records, referential integrity, and business-rule validation.
- Improved data quality to 99% in the project through resolving duplicate, missing, and inconsistent records.
Project details and metrics reflect the project descriptions in my CV. Repository links can be added to each project heading as the code and documentation are published.
flowchart LR
A["Source Systems"] --> B["Ingestion & Integration"]
B --> C["Bronze<br/>Raw Data"]
C --> D["Silver<br/>Clean & Validated"]
D --> E["Gold<br/>Business Models"]
E --> F["Analytics & BI"]
D --> G["AI / RAG Applications"]
H["Governance & Metadata"] -.-> C
H -.-> D
H -.-> E
I["Quality & Observability"] -.-> B
I -.-> D
My approach connects data engineering with quality, governance, analytics, and AI readiness across the data lifecycle.
- M.Sc. Data Science — Cairo University, 2026–present
- B.Sc. Computer and Control Engineering — Higher Institute for Engineering and Technology, 2019–2024
- AWS Certified Cloud Practitioner
- IBM Data Engineering Professional Certificate — 2025
- Salesforce Certified Tableau Desktop Foundations
- ITI Data Engineering Track — Data warehousing, ETL/ELT, Informatica, Hadoop, Spark, and Databricks
Interested in data engineering, data management, governance, analytics, or AI-enabled data solutions? Feel free to connect.
- LinkedIn: linkedin.com/in/mustafa-rezk-
- Email: mustafarezk436@gmail.com
- CV: Download Mustafa Rezk's Data Engineer CV
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Mustafa_Rezk_Data_Engineer.pdfto the root of this same profile repository. Keep the filename exactly the same as the link above, or update the link to match your chosen filename.
Building reliable data foundations for better decisions and intelligent applications.