Revathi Nandini P
About Revathi Nandini P
Revathi Nandini P is a Data Engineer at Fannie Mae in Knoxville, Tennessee, with extensive experience in cloud environments, databases, and machine learning algorithms.
Current Position at Fannie Mae
Revathi Nandini P works as a Data Engineer at Fannie Mae in Knoxville, Tennessee, contributing significantly to the organization's data management initiatives. She brings her extensive experience with cloud environments and database management to the role, ensuring the accuracy and efficiency of data processes within the company.
Previous Work Experience
Revathi Nandini P has a robust background working as a Data Engineer for various notable companies. She was employed at Athene in West Des Moines, Iowa, from 2021 to 2022. Prior to this, she worked at State Farm in Peoria, Illinois, from 2019 to 2021, and at Delhivery in Hyderabad, Telangana, India, from 2016 to 2018. She began her data engineering career at CMC LTD as a Python Developer in Hyderabad, Telangana, India, from 2014 to 2015.
Educational Background
Revathi Nandini P earned a Master of Science degree in Analytics and Modeling from Valparaiso University between 2019 and 2020. Prior to this, she completed a Bachelor of Science degree in Mathematics from St. Francis College for Women, studying from 2012 to 2015. Her educational foundation provides her with the analytical skills necessary for her data engineering roles.
Specialization in Data and Cloud Technologies
Revathi Nandini P has in-depth expertise with Google Cloud Platform services, including BigQuery, GCS, and Dataflow. She has over four years of hands-on experience with Snowflake databases across all five editions. Additionally, she is proficient with DevOps tools like Jenkins and Bitbucket, databases such as PostgreSQL, Teradata, Oracle, and DB2, and utilizes integrated development environments such as Anaconda, PyCharm, and Sublime Text.
Experience with Machine Learning and Data Modeling
Revathi Nandini P has significant experience in machine learning, specifically with algorithms in Supervised and Unsupervised learning, including Regression, Classification, Clustering, and Dimensionality reduction. She is adept in creating Star and Snow-Flake Schemas, FACT and Dimensions Tables, and Physical and Logical Data Modeling using Erwin. Her skills extend to data migration using Kubernetes and Lambda, workflow scheduling with ControlM, and application management with the Kubernetes container orchestration system.