I’m Sagar Umesh Dama, a Full Stack Software Engineer and MCS student at NC State with 2+ years of experience at Morgan Stanley. I specialize in building scalable backend systems, distributed systems, and intelligent data pipelines using Java, Spring Boot, Angular, Python, and AWS. My work spans AI integrations, cloud-native development, and performance optimization—backed by strong testing, DevOps, and database skills. Passionate about solving real-world problems with clean, efficient, and maintainable code.
Full Stack Software Engineer
Highlights
Pioneered the Modernization of legacy ColdFusion Application to React-Spring Boot(Java), achieving a 75% reduction in latency.
Normalized the V1.0 SQL schema to improve database efficiency and integrity. Established CI/CD pipeline using Github Actions.
Led the team to achieve 90% Jest Ul coverage and 95% Backend coverage using Junit & Selenium, ensuring high-quality code.
Engineered an async Data Engineering ETL pipeline using Python Django & Celery with Redis to fetch pest data from sources like EPPO, IPCC, CABI, Inaturalist to automate the pest information retrieval process.
Developed a AI RAG Chatbot using lama 3.7b model and MongoDB vector store to serve user queries for different pest information
Software Engineer Intern
Highlights
Designed, developed and tested an interactive Javascript React web chart component using MUI Charts, Jest and Cypress, increasing user 60% for the Rent-vs-Buy calculator.
Contributed to the migration of search functionality in Rocket Blogs from Google Cloud Search to an Al-enabled intelligent search using AWS Kendra and .Net/C#, improving search accuracy by 25% and reducing query response time by 30%.
Leveraged the Al capabilities of AWS Bedrock to prompt-engineer the Llama-3B model for summarizing blog content, boosting customer engagement by 25% through the delivery of quick and digestible summaries.
Full Stack Software Engineer Consultant
Highlights
Collaborated with business stakeholders and analysts to improve the analysis report generation speed and accuracy by 60% by developing a stock analysis tool utilizing Typescript Angular HighCharts, AgGrid and Spring Boot & Hibernate API Architecture.
Designed an ETL pipeline to get data from Factset SnowFlake upstream and store it into a KDB database using Hadoop and Spark.
Designed an asynchronous caching service to cache KDB queries into MongoDB using Kafka, reducing load on KDB and improving application performance by 80%.
Administered the end-to-end system testing to achieve 93% SonarQube Coverage and 100% Junit and Cucumber Coverage.
Containerized the software with Docker and automated CI/CD pipelines using Jenkins and AWS Kubernetes.
Established trace monitoring using Open Telemetry and Jaeger and Log and system monitoring using Splunk, Prometheus and Grafana.
Developed FastAPI endpoints with Python and Pandas to automate data comparison across different database types like MongoDB, SQL, KDB and ElasticSearch.
Application Development Intern
Highlights
Optimized the operational cost by 70% by outlining API endpoints via Django (Python) and SQL Admin Dashboard with the help of OAuth2 Authentication, AWS S3 and AWS RDS, AWS EC2 enabling admin to manage static as well as dynamic customer data.
Pioneered the adoption of video-on-demand features to reduce operational cost and increase client engagement by 60% by integrating Cloudfront, AWS Lambda and MediaConvert for streaming content on Flutter App. Integrated Google sign-in and Razor-Pay in the app
Master of
Computer Science
Grade: 3.9/4
Courses
Design and Analysis of Algorithms
Software Engineering
Automated Learning and Data Analysis
Software Security
Object Oriented Design and Development
Neural Networks
Database Systems
GenAl for Software Engineering
Distributed Systems
Bachelor of Technology
Computer Science
Grade: 3.7/4
Courses
Data Warehousing
Operating Systems
Adv. DBMS
Project Management
Artificial Intelligence
System Security
Java, Python, PHP, Ruby, MySQL, PostgreSQL, MongoDB, ElasticSearch, C++, C#, KDB+, GraphQL, Golang.
Spring Security, Spring Boot, Django, Angular, Flask, Node, Junit, Flutter, HTML5, CSS, AJAX, Algorithms, Git, Jira, Docker, Hadoop, Jenkins, Microservices, APIs, OOP, Selenium, JavaScript, Cypress, Jasmine, SnowFlake, TypeScript, Agile, AWS, Mockito, PyTest, Pandas, Numpy, Spark, Rails, React, Unix & Linux, DevOps, Gradle, RabbitMQ, SonarQube, ZAP tool.
Summary
Designed an automated logging tool that uses LLMs to predict optimal log placements in Java programs. Generated contextually relevant log statements without modifying the existing code structure. Enhanced software observability and developer productivity, outperforming existing techniques based on BLEU and ROUGE metrics
Summary
Engineered a predictive auto scaling framework using LSTM to forecast CPU usage from Prometheus metrics, enabling proactive scaling on Kubernetes EKS clusters. Developed a resource pressure model linking CPU usage to SLO violations (failure rate), optimizing node allocation to maintain failure rate under a 15% threshold. Enhanced scaling decisions beyond traditional metrics by incorporating failure rate predictions, improving reliability for read/write-heavy RAG workloads designed using Flask, PostgreSQL and PgVector.