Chandra Maurya

Developed a data pipeline for multiple ASINs, storing data in Redshift via Python, and deployed on Airflow. Configured Zyte Data transfer from S3 to Redshift for efficient analytics. Automated report extraction, storing data in Redshift for streamlined analysis. Collected, stored, and automated data retrieval from Google Ads and Amazon Ads platforms, and deployed on Airflow. Implemented Tableau replication method using Python for advanced data visualization. Extracted NoSQL data from CosmosDB and stored it in Redshift in tabular form with incremental mode. Created and maintained a data pipeline, ingesting data from various sources using Python, S3, Redshift, and Airflow. Utilized Talend to efficiently ingest data from diverse sources into the client’s data warehouse, ensuring seamless integration and reliable data transfer.

Developed a Python-based reporting system for Salesforce data. Designed and implemented the data pipeline architecture for Google Ads and Amazon Seller data to RDS MySQL using Python, AWS, and Google Cloud. Ingested data from disparate data sources using SQL and Google Analytics API using Python to create data views for BI tools like Tableau. Fetched data from MongoDB using MongoDB query and automated the report using Python and Crontab. Used Selenium Python library to extract desired data from different E-commerce websites.