ARPITHA S

Proficient in writing basic SQL queries to retrieve and manipulate data from relational databases. Experience in extracting data from databases and generating reports to support business operations. Understanding of relational database design principles, including table creation, primary and foreign keys. Conducted data cleaning and validation using SQL to ensure data quality and accuracy. Skilled in performing basic joins and subqueries to combine data from multiple tables and perform complex queries. Designed and implemented end-to-end data pipelines, ensuring efficient extraction, transformation, and loading (ETL) processes. Established a data pipeline where files arriving at an S3 location are ingested and transformed using PySpark, followed by loading the processed data back into both S3 and an Aurora database, seamlessly integrating PySpark, SQL, and Python technologies within the pipeline. Developed and maintained data pipelines on AWS, utilizing services such as S3, Glue to ensure reliable and scalable data storage, ETL, and processing. Collaborated with cross-functional teams to understand data requirements and implement solutions to meet business objectives. Utilized SQL and Python to perform data analysis, troubleshoot issues, and optimize performance of data pipelines. Worked collaboratively with senior data engineers and documented SQL scripts and database changes for project tracking.