Project Overview:
Delivered end-to-end ETL solutions for complex data integration needs, focusing on optimizing Snowflake operations and implementing automated data quality processes. Architected robust data pipelines handling large-scale data processing while ensuring data accuracy and accessibility.
Key Responsibilities:
- Engineered ETL pipelines using Databricks and AWS S3
- Optimized Snowflake operations through strategic feature implementation
- Developed and maintained production data load processes
- Automated user access management in Tableau
- Created comprehensive data quality frameworks
Technical Environment:
- Cloud Platform: AWS (S3, Glue)
- Data Processing: Databricks, MongoDB
- Data Warehouse: Snowflake
- Visualization: Tableau
- Languages: Python, SQL
Major Achievements:
- Advanced ETL Pipeline Development
- Challenge: Need to process and integrate massive datasets from multiple sources with varying formats and update frequencies. Legacy processes were manual and couldn't handle increasing data volumes efficiently.
- Solution & Impact: Engineered scalable ETL pipelines using Databricks and AWS S3:
- Implemented parallel processing for improved performance
- Built robust error handling and recovery mechanisms
- Created automated validation checks
- Reduced processing time by 65%
- Improved data reliability and completeness
- Handled 10M+ daily records efficiently
- Automated Access Management
- Challenge:Manual Tableau user access management was time-consuming and error-prone, creating security risks and administrative overhead.
- Solution & Impact: Developed Python automation for access management:
- Created role-based access control system
- Implemented automated user provisioning/deprovisioning
- Built audit logging and compliance reporting
- Reduced admin time by 80%
- Improved security compliance
- Eliminated access-related incidents
Skills Advanced:
- ETL Pipeline Design
- Performance Optimization
- Process Automation
- Data Quality Management
