Scalable Architecture for Test Data Generation and Migration
Designed and implemented a scalable architecture for test data generation and migration to Azure, ensuring data consistency and optimizing performance. Developed strategies to handle large volumes of test data, ensuring seamless migration without compromising data integrity. Collaborated with cross-functional teams to integrate the new architecture with existing systems, enhancing overall efficiency and reliability. Provided architectural guidance on SQL query design, emphasizing referential integrity to support enhanced analytics and reporting capabilities.
Python-Based ETL Pipeline
Architected a Python-based ETL (Extract, Transform, Load) pipeline to replace a legacy solution, significantly increasing processing efficiency for millions of records. Implemented robust data extraction, transformation, and loading processes, ensuring data accuracy and consistency throughout the pipeline. Optimized the pipeline for performance, reducing processing time and enhancing the ability to handle large datasets. Conducted thorough testing to validate the pipeline’s functionality and reliability, ensuring it met business requirements.
Azure Governance Policies
Defined and enforced Azure governance policies during migration, incorporating Synapse, Delta Tables, and Bicep to ensure seamless operations. Established guidelines and best practices for resource management, security, and compliance within the Azure environment. Leveraged Synapse and Delta Tables to enhance data processing and analytics capabilities, supporting complex data workflows. Utilized Bicep to automate and streamline infrastructure deployment, ensuring consistency and reducing manual effort.