A comprehensive collection of data engineering projects, proofs-of-concept (POCs), and proofs-of-knowledge (POKs) demonstrating expertise in building scalable data pipelines, ETL/ELT solutions, and data platform architectures using Python, Spark, SQL, and cloud platforms.
- About
- Key Features
- Technology Stack
- Projects
- Skills Demonstrated
- Getting Started
- Contributing
- License
- Contact
This repository serves as a portfolio of my data engineering work, showcasing practical implementations of:
- ETL/ELT Pipelines: End-to-end data pipelines from ingestion to consumption
- Big Data Processing: Large-scale data processing using distributed computing
- Data Warehouse Design: Dimensional modeling and data warehouse architectures
- Cloud Data Platforms: Cloud-native data solutions on AWS, Azure, and GCP
- Data Quality & Governance: Data validation, monitoring, and governance frameworks
- Real-time Processing: Stream processing and event-driven architectures
Experience Level: 6+ years in Data Engineering & Analytics
- ✅ Batch and real-time data ingestion pipelines
- ✅ Incremental and full-load strategies
- ✅ Error handling and retry mechanisms
- ✅ Pipeline orchestration and scheduling
- ✅ Data quality validation and monitoring
- ✅ Distributed data processing with Apache Spark
- ✅ Cloud-native architectures (AWS, Azure, GCP)
- ✅ Containerized applications with Docker
- ✅ Infrastructure as Code (Terraform)
- ✅ Microservices-based data platforms
- ✅ Modular and reusable code
- ✅ Comprehensive testing (unit, integration, E2E)
- ✅ CI/CD pipeline integration
- ✅ Detailed documentation
- ✅ Performance optimization
- Python 3.9+ - Primary language for data engineering
- SQL - Data transformation and querying
- Bash - Shell scripting and automation
- Apache Spark - Distributed data processing (PySpark, Spark SQL)
- Pandas - Data manipulation and analysis
- dbt - Data transformation framework
- Apache Kafka - Stream processing
- Apache Airflow - Workflow orchestration and scheduling
- Prefect - Modern workflow management
- AWS - S3, Glue, EMR, Lambda, Redshift, Athena
- Azure - Data Factory, Databricks, Synapse Analytics
- GCP - BigQuery, Dataflow, Cloud Composer
- Snowflake - Cloud data warehouse
- Amazon Redshift - AWS data warehouse
- Google BigQuery - Serverless data warehouse
- PostgreSQL - Relational database
A production-ready ETL framework for ingesting data from multiple sources, transforming it, and loading it into a data warehouse.
Tech Stack: Python, Apache Airflow, Snowflake, AWS S3
Key Features:
- Configurable data sources (APIs, databases, files)
- Incremental data loading strategies
- Data quality validation framework
- Error handling and alerting
- Performance monitoring and logging
Stream processing pipeline for handling real-time data events using Apache Kafka and Spark Streaming.
Tech Stack: Apache Kafka, Apache Spark (Streaming), Python, AWS
Key Features:
- Real-time event ingestion
- Stream processing and aggregation
- Windowing operations
- Fault tolerance and exactly-once semantics
Implementation of a modern data lake architecture with multiple processing layers (Bronze, Silver, Gold).
Tech Stack: AWS S3, AWS Glue, Apache Spark, Python
Key Features:
- Raw data ingestion (Bronze layer)
- Cleansed and conformed data (Silver layer)
- Business-ready aggregations (Gold layer)
- Metadata management
- Data cataloging and discovery
Migration project from on-premises database to cloud data warehouse with minimal downtime.
Tech Stack: PostgreSQL, Snowflake, Python, dbt
Key Features:
- Schema conversion and optimization
- Data migration scripts
- Validation and reconciliation
- Performance tuning
Automated data quality monitoring system with configurable rules and alerting.
Tech Stack: Python, Great Expectations, Apache Airflow
Key Features:
- Configurable quality rules
- Automated validation checks
- Quality scorecards and dashboards
- Alerting and notifications
- ETL/ELT pipeline design and development
- Data modeling (dimensional, normalized, denormalized)
- Data warehouse design and optimization
- Big data processing with Spark
- Stream processing and real-time analytics
- Cloud-native architecture design
- Infrastructure as Code (Terraform)
- Containerization and orchestration
- Serverless computing
- Cost optimization strategies
- Data validation frameworks
- Data lineage tracking
- Metadata management
- Data cataloging
- Quality metrics and monitoring
- Python 3.9 or higher
- Docker (optional, for containerized environments)
- Cloud account (AWS, Azure, or GCP) - for cloud-based projects
- Apache Spark (for Spark-based projects)
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Clone the repository:
git clone https://gitlab.com/fuadonates/data-engineering.git cd data-engineering -
Create virtual environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install dependencies:
pip install -r requirements.txt
While this is primarily a portfolio repository, contributions and suggestions are welcome!
- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Fuad - Senior Data Engineer & Data Analyst
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Made with ❤️ by Fuad