Poorva Barve barveP

Hi there! I'm Poorva Barve

About Me | Resume

class Poorva:
    """From dentist to developer — I debug code now, not cavities."""

    def __init__(self):
        self.location  = "Seattle, WA"
        self.education = "MS Computer Science @ Northeastern (4.0 GPA)"
        self.email = "poorvabarve@gmail.com"

        self.expertise = [
            "Distributed and Scalable Systems",
            "Backend Development",
            "Full Stack Development",
            "Cloud Computing & AWS",
            "Data Engineering",
            "AI Agents"
        ]

    def tech_i_love(self):
        return {
            "languages":  ["Python", "Java", "TypeScript", "JavaScript", "C/C++"],
            "backend":    ["Spring Boot", "Node.js", "Express.js", "Flask"],
            "data":       ["Airflow", "Spark", "Snowflake", "Kafka", "DBT", "Pandas"],
            "databases":  ["PostgreSQL", "MongoDB", "Redis", "MySQL"],
            "cloud":      ["AWS (Lambda, EC2, SQS, S3)", "Docker", "Kubernetes", "Terraform"],
            "frontend":   ["React.js", "HTML/CSS"],
        }

LeetCode

Experience

Northeastern University — Teaching Assistant, Object-Oriented Design (CS3500)

Jun 2025 - Aug 2025 • Seattle, WA

Built a scalable hackathon management app with Java Spring Boot, React, MongoDB & AWS
Enhanced dashboard performance by 80% with Kafka multi-partition user activity tracking
Automated build-failure alerts via Slack, improving triage response time by 75%

Northeastern University — Teaching Assistant, Cloud Computing (CS4973)

May 2025 - Jun 2025 • Seattle, WA

Built serverless image pipeline with API Gateway, SQS, Lambda & S3
Developed real-time chat backend (WebSockets + Redis pub/sub) supporting 500+ concurrent users
Load-tested API infra with ALB & EC2 auto-scaling handling 5K req/sec at peak
Mentored 40+ students through hands-on AWS labs toward AWS Academy certification

Featured Projects

Stock Market ETL Pipeline

Data Engineering • Dec 2025

Orchestrated 4-stage DAG in Airflow for S&P 500 (500 tickers, ~2M records/month)
Normalized raw JSON into fact/dimension tables with 12 quality checks
Reduced daily pipeline runtime from 60 min to 8 min with incremental loads

View on GitHub →