I'm a graduate student in computer science at NYU Courant, diving into the latest technologies and techniques in distributed systems, databases, and data engineering. I am looking for full-time job opportunities where I can apply my skills to build low-latency, highly scalable software applications.
I worked as the Recitation Leader for Prof. Benjamin Goldberg's Computer Systems Organization course in Spring 2024.
I taught students weekly, covering broad aspects of computer systems, including computer architecture, C programming, assembly code, and logical circuits. This involved revising the material covered in lectures, as well as extending it by introducing new ideas
I worked as the Graduate Teaching Assistant for Prof. Lakshminarayanan Subramanian's Real-time Big Data Applications course in Spring 2024.
I was involved in helping introduce the students to various big data processing systems, ranging from the traditional MapReduce framework and Spark/Kafka, to cutting-edge systems such as VBASE and SageDB. This was primarily motivated by paper readings, followed by discussions in class. My responsibilities also included preparing & grading assignments and mentoring students during their semester-long project.
I worked as the Graduate Teaching Assistant for Prof. Dennis Shasha's Advanced Database Systems course in Fall 2023.
I helped students understand database system internals better, by answering questions, presenting various tools required for the course, and grading and providing feedback on their assignments and projects.
Built and maintained a high-performance gRPC API server in Go, responsible for providing aggregated customer profile data to other internal microservices. Optimized server to handle a peak load of 400K requests per minute.
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Led migration of infrastructure and applications from the on-premises data center to AWS. Spearheaded data pipeline revamp, bringing costs down by 63%.
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Proposed and implemented an alert system for low ticket availability, increasing weekday ticket sales by 8%. Led the effort across multiple teams and collaborated with stakeholders to implement an extendable, rule-based notification system in Go by leveraging MariaDB, AWS Redshift, and CleverTap.
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Engineered and optimized Spark ETL pipeline on AWS EMR for processing 2 TB data daily, enabling customer profiling and segmentation, used for powering Personalisation, Discovery, Rewards, and Ad-Tech platforms.
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Established data acquisition flow from third-party vendors to enhance user categorization and targeting. Integrated with existing segmentation pipeline for Rewards and Ad-Tech platforms, increasing ad-based revenue by 7%.
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Built and maintained scalable backend systems to enhance the user journey platform, which comprised over 100M profiles and 300M sessions. Improved the customer onboarding, login, authentication, purchase history, and device management flows for the end user.
