Hi, I'm Sneha Grian Joshua

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Building scalable, fault-tolerant distributed systems and cloud-native services with a focus on performance optimization and reliability

About Me

Software Engineer with strong foundations in data structures, algorithms, and object-oriented design. Experienced in building scalable, fault-tolerant distributed systems and cloud-native services using Java, Python, and AWS. Proven ownership of features across the full development lifecycle, with a focus on performance optimization, reliability, and delivering high-quality software in Agile environments.

Full-Stack Development

Expert in building scalable web applications using React, Node.js, Java, and Python

Cloud & DevOps

Experienced with AWS, Azure, Docker, Kubernetes, and CI/CD pipelines

System Design

Specialized in distributed systems, microservices architecture, and fault-tolerant design

10+
Production Projects
Delivered to clients
30%
Latency Reduction
In microservices
40%
Efficiency Gain
User tracking effort
72%
ML Accuracy
Cyberbullying detection

Work Experience

Software Engineer

FOSSFREAKS Private Limited
Chennai, India
November 2022 - December 2023
  • Designed, developed, and delivered cloud-ready, full-stack web applications using React.js, Node.js, and Express, contributing to 10+ production client projects across multiple domains
  • Built scalable, fault-tolerant RESTful APIs using efficient data structures and object-oriented design principles, improving system performance, latency, and reliability
  • Developed reusable, maintainable frontend components with React Context API and integrated backend services to ensure consistent state management and responsive user experiences
  • Implemented an AI-powered expense recommendation engine using OpenAI APIs, applying backend logic and data processing to deliver personalized user insights
  • Owned features end-to-end, from design and implementation to deployment and production support, including debugging issues, writing Jest tests, and supporting CI/CD pipelines

Research Intern

EinNel Technologies
Chennai, India
February 2022 - October 2022
  • Optimized Spring Boot microservices in a distributed backend environment, reducing API latency by 30% and improving scalability and responsiveness
  • Designed and implemented asynchronous and reactive REST APIs, increasing backend throughput, fault tolerance, and system resilience
  • Contributed to ML-driven automation initiatives and full-stack bug fixes across React, Java, and PostgreSQL, reducing backend processing time by 25%
  • Collaborated with cross-functional teams to debug production issues, improve system reliability, and support iterative delivery in an Agile development workflow

Education

Master of Science in Information Technology

Arizona State University
Tempe, USA
January 2024 - December 2025

GPA: 3.79

Relevant Coursework:

Natural Language ProcessingData Visualization and Reporting for ITAnalyzing Big DataAdvanced Big Data Analysis and Artificial IntelligenceAdvanced DB Management SystemsInfo Systems DevelopmentCloud Architecture

Bachelor's in Computer Science and Engineering

Anna University
Chennai, India
August 2018 - April 2022

GPA: 3.71

Skills & Technologies

Programming Languages

Python
Java
JavaScript
TypeScript

Frontend

React
Next.js
Tailwind CSS
Redux

Backend & Frameworks

Node.js
Express
Spring Boot

Databases

MongoDB
PostgreSQL
MySQL

Cloud & DevOps

AWS
Docker
Kubernetes
Jenkins
Git

Core Concepts

Data Structures & Algorithms
Object-Oriented Design
Distributed Systems
Microservices Architecture
CI/CD
Agile & SDLC

Academic Projects

Budgetron - Personal & Group Financial Management Platform

August 2025
React NativeExpress.jsNode.jsMongoDB
  • Collaborated with a team to build a React Native + Express.js application for expense tracking, group payments, and financial goal management
  • Enabled collaborative expense sharing, automated bank statement imports, and exportable reports (Excel/PDF), reducing manual tracking effort by 40%
40% reduction in manual tracking

Cyberbullying Predictive Analysis on Twitter Data

January 2022 - April 2022
Team Size: 3
PythonRandom ForestMachine LearningData Processing
  • Developed a Random Forest-based classification model to detect harmful content, achieving 72.2% accuracy
  • Supported by scalable data preprocessing pipelines for efficient analysis of large Twitter datasets
72.2% accuracy achieved

Get In Touch

Contact Information

Feel free to reach out to me for opportunities, collaborations, or just to connect!