Engineered robust data pipelines that seamlessly extracted, transformed, and loaded data from diverse sources into analytics platforms, enabling data-driven decision making and visualization capabilities.
🚀 Core Contributions
🧠 ETL Pipeline Architecture
Custom Data Pipeline: Developed and optimized Python-based ETL framework for multi-source data integration
Modular Component Design: Created containerized data-processor modules for flexible deployment
Data Quality Management: Implemented validation and transformation logic to ensure consistency and accuracy
Performance Optimization: Enhanced extraction, transformation, and loading processes for improved scalability
☁️ Cloud Integration & Deployment
AWS Infrastructure: Leveraged EC2, S3, and CodeBuild for robust pipeline hosting and deployment
CI/CD Implementation: Established automated build and deployment pipelines for data processing components
Docker Containerization: Packaged pipeline components for consistent execution across environments
Multi-Source Connectivity: Built secure connections to SharePoint, Snowflake, and Redshift data sources
🔄 Data Transformation Framework
Reusable Function Library: Developed modular Python transformations for data mapping and standardization
Staging Layer Architecture: Created intermediate data structures for efficient processing