Developed a pipeline to extract data from diverse sources, including SharePoint, Azure, and Google Cloud Storage
Created SQL scripts to load data from multiple tables and apply complex transformations, such as joins, unions, and other operations, tailored to specific requirements
Automated the ETL process using Composer DAG which resulted in reduction of manual workload
Designed ETL pipelines using Python and Flask APIs to extract data from multiple sources and load it into BigQuery
Established a scalable data lake architecture to store, process, and manage large datasets, enabling high-performance analytics
Established CI/CD practices for data pipelines using Cloud Build and Git, ensuring continuous integration and streamlined deployment
Created comprehensive documentation for ETL processes, data flows, and architecture, and conducted training sessions for new team members and stakeholders
Client Data Scientist - Marketing Team
Infratech
01.2021 - 07.2022
Turned data into actionable insights, providing C-suite stakeholders with insightful recommendations to streamline business operations and improve customer experience
Generated statistical reports and visualizations, providing key insights for more than 20 marketing campaigns and initiatives, including A/B testing, customer retention, brand awareness, and global expansion
Worked with senior leadership to develop and implement digital marketing strategy, identifying and implementing new tactics to improve campaign performance by 50%, resulting in 250% increase in revenue from search marketing campaigns