Sri Krishna Chaitanya Kommalapati

Master's

Business Analytics

Sonata Software

Software Development AND Support Engineer

- Managed implementation and support projects as a D365 Admin in early stages, implementing proactive and efficient problem resolution for general, LCS, and performance issues in Dynamics 365 F&O environments - Demonstrated expertise in deploying and managing cloud-hosted environments for Dynamics 365 F&O - Established and maintained DevOps pipelines for automation of database refresh and code deployment - Implemented auto-scaling strategies to dynamically adjust resources based on workload demands, enhancing - Implemented robust security measures and compliance controls within cloud-hosted Dynamics 365 F&O - Conducted regular security audits and vulnerability assessments, proactively addressing and mitigating potential - Created DevOps data pipeline to take daily backups of SQL Tables into S3 bucket, saving 3 hours of SDE weekly - Designed DB schema using normalization techniques and removed data redundancy by 40% compared to existing - DB2 - Academic Projects - Cloud-Infused E-Commerce Overhaul - Spearheaded the deployment and configuration of EC2 instances, ensuring a robust hosting environment for the e-commerce platform with a focus on scalability and high performance - Implemented Elastic Load Balancer (ELB) to evenly distribute incoming traffic across EC2 instances, enhancing the e-commerce platform's scalability and fault tolerance - Established a reliable Relational Database Service (RDS) to store and manage e-commerce data, emphasizing data - Designed and implemented a serverless processing pipeline using Amazon S3 for storage and Lambda functions for event-triggered tasks, optimizing system efficiency and reducing operational complexities - ETL Pipeline - DDB, AWS Glue, Athena, S3, SQL - Created a ETL pipeline to export data from AWS DynamoDB to S3 and created a Glue table for querying through Athena - Established scheduled AWS Quick sight Dashboard for daily analysis updates with the latest snapshot - Used Naive Bayes, KNN, Logistic regression, SVM and Random Forest Classifier algorithms in model building - Armanino LLP - Html, React JS, NodeJS, DDB, Machine Learning, R - Designed multiple webpages using to collect data and store them in AWS to predict carbon emissions, to promote - Developed Machine Learning models for predicting sustainability and aiming to increase it by 15% this year - Created 6 React components and reused them in both the user and admin dashboard saving 1 week of development