Bandhavya Parvathaneni

Cloud SRE & Data Analytics @ Marvell Technology

About

I graduated with a Bachelor of Science in Computer Science and a minor in Business Administration from Chapman University, where I built a strong foundation at the intersection of technology and business. At Marvell, I now focus on cloud and data analytics, working across HR, Legal, and Marketing to design scalable solutions and deliver actionable insights. My background as a Cloud Site Reliability Engineer has shaped my expertise in infrastructure and automation, and I continue to explore how strategy, data, and technology come together to drive impact. I’ve previously participated in hackathons and collaborative tech initiatives, and I’m passionate about using engineering and analytics to fuel innovation, with long-term goals of contributing to tech strategy or building a venture of my own.

Country

United States

City

San Francisco Bay Area

Industry

Consumer Electronics

Skill

Google Cloud Platform (GCP), Agentic AI Development, databricks, Microsoft Power BI, Snowflake, Grafana, MySQL, PhpMyAdmin, Verilog, Machine Learning, HashiCorp Vault, Amazon EC2, AWS Identity and Access Management (AWS IAM), AWS Command Line Interface (CLI), Cloud Computing, Terraform, Artificial Intelligence (AI), Amazon Bedrock, Jenkins, Amazon Relational Database Service (RDS)

Experience

Cloud SRE & Data Analytics

Marvell Technology

2025-5 - Present · 1 yr 5 mos

Santa Clara, California, United States

Key Contributions: - Built an in-house EC2 autoscaling platform for engineers, cutting manual infra work 90%. - Architecting the same intelligent autoscaling system for GCP. - Developed real-time monitoring stack (Flask + Prometheus + RDS). - Delivered executive analytics platforms like Legal360 for spend, risk, and compliance insights. - Unified enterprise data across Snowflake, Databricks, and Power BI for governed reporting.

Cloud Site Reliability Engineer

Marvell Technology

2025-1 - 2025-5 · 5 mos

Santa Clara, California, United States

Cloud Site Reliability Engineer Intern

Marvell Technology

2024-5 - 2024-8 · 4 mos

Santa Clara, California, United States

During my internship, I developed an AI/ML web tool designed to automate machine performance benchmarking and recommend optimal machine types for specific tasks, enhancing user efficiency and decision-making in both on-premises and cloud environments. I built a historical profiling database using a Jenkins pipeline that connected to on-premises machines or instantiated EC2 instances via Terraform. The pipeline executed benchmarking tools such as Sysbench and Fio, inputted the results into an RDS, and then safely disconnected or terminated the instances, thereby streamlining the benchmarking process.