Dipendra Chandel

Data Analyst skilled in Python, SQL, and data visualization, with expertise in extracting insights from complex datasets and using PostgreSQL for efficient data storage. Proficient in workflow optimization to support data-driven decisions, leveraging BeautifulSoup for data extraction. Demonstrated ability in analyzing and interpreting data to enhance operational efficiency. Strong foundation in statistical analysis, reporting, and visualization to effectively communicate findings and drive actionable business outcomes.

Amazon Book Data ETL Pipeline

• Designed an ETL pipeline using Apache Airflow for scheduling and BeautifulSoup for web scraping of Amazon book data (title, author, price, rating). Cleaned and structured the data before storing it in PostgreSQL for efficient querying.

Superstore Orders Data Analysis on AWS

• Built an end-to-end ETL data pipeline on AWS to analyze superstore order data, utilizing S3 for data storage, AWS Glue for cataloging, and Athena for querying. Created insightful visualizations in Amazon QuickSight to support data-driven insights.

Apple Purchase Patterns Data Analysis with PySpark

• Developed a PySparkdata pipeline to analyze purchase patterns in Apple transaction data. Employed modular abstract classes to handle data formats like CSV, Parquet, and Delta tables, using SQL window functions and joins to aggregate and process customer transactions.