• Around 4 years of Progressively responsible experience in Data Engineering, Data Analysis, ETL, and Data warehousing, Business Intelligence background in Designing, Developing, Analysis, Implementation, and post implementation support of DWBI applications. • Worked on architecting, designing, and implementation of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse. • Used Python, implemented multiple machine learning techniques such as Decision Tree, Naive Bayes, Logistic Regression, and Linear Regression to evaluate the accuracy rate of each model. • Analyzed and transformed data using various SQL functionalities using MySQL server to generate data extracts and reports. • Conducted data analysis experiments, A/B testing, and statistical modeling to optimize business processes, improve customerexperience, and increase operational efficiency. • Stayed up to date with the latest cloud technologies, data analytics tools, and industry trends, and recommended innovative solutionsto enhance data analytics capabilities. Developed data wrangling through data gathering and querying process over SQL and implemented data cleaning process with Rand Python. Worked with a diverse set of clients all over the world, identifying and analyzing Dynamic Values, and managing development with agile methodology. • Streamlined data processing by automating ETL workflows in Informatica, DataStage, and Ab Initio, resulting in a 30% reduction in manual handling and improved data processing speed. This initiative enhanced data quality and integrity within Snowflake, with additional integration of Informatica with Airflow for enhanced workflow management. • Developed backend applications to integrate real-time data and automate data loading processes using Oracle SQL, PL/SQL, T-SQL, and Unix Shell scripting. • Engaged in SQL performance tuning, troubleshooting, and database administration to ensure optimal database performance. • Designed, optimized, and secured CI/CD pipelines using Apache Airflow, Docker, Kubernetes, and SQL, collaborating cross-functionally for efficient data processing and analysis. • Achieved operational cost reduction and improved data processing speed by 20% through pipeline optimizations, while mentoring junior data engineers to ensure team growth and development. • Utilized Snowflake's architecture for external data sharing, supporting data monetization strategies, and collaborated on end-to-end data pipelines, optimizing ETL job performance and modernizing infrastructure, including ETL processes.