Raman Gupta

Designed and implemented optimized data warehouse tables to efficiently store and manage large volumes of data. Improved query performance by 30% by implementing partitioning strategies. Developed comprehensive schema designs that effectively supported the reporting and analytics requirements of the project. Demonstrated expertise in data extraction, transformation, and loading (ETL) by writing scripts to extract data from diverse sources and load it into DWH. Implemented SCD Type 2 methodology for incremental data processing, ensuring accurate and historical representation of changing data over time. Reduced data update time by 70% by implementing efficient SCD techniques. Built a robust data pipeline that seamlessly moved data from the silver layer to the gold layer, ensuring accurate and reliable data for reporting purposes. Proficiently utilized a range of ETL tools and technologies, including PySpark, SQL, Python, and Azure Data Factory (ADF), to streamline the data integration and transformation processes.

Conducted data analysis as a Research Intern in the Data & Operations department, utilizing tools such as Excel. Objective: To analyzing the dataset, gather insights about business, customer behavior and preferences. Key Achievement: The dataset shows clear seasonality trends in sales. Objective: To build an insight about movie released in past and derive recommendation to produce new movies. Key Achievements: Found down trend in movies released in past years, most popular genre is drama.

Course Modules: Data Analysis using SQL | Introduction to Python | Introduction to Machine Learning | Data Visualization | Telecom Churn Case Study | IMDB Movie Data Analysis