Productionized and maintained a self-service analytics layer on AWS Glue Data Catalog and Lake Formation, giving teams governed access to data without external BI licensing. Managed ETL through Terraform, and designed Spark-based Glue pipelines that pulled in data from a wide range of sources on an automated schedule, with lineage teams could trust. Also exposed the platform to LLMs through an MCP server, and owned platform reliability - resolving production incidents and tuning slow queries as they came up.

Designed the data models and SQL layers behind the company's analytical dashboards, and automated recurring stakeholder reporting through scheduled Glue workflows. Fine-tuned an LLM to predict gift occasions, replacing manual tagging with automated inference, and hardened pipelines with monitoring, validation, and access controls to keep jobs stable and quick to recover.

Mentored students across quantitative courses, using structured sessions to boost engagement.

Built an event-driven AI news platform that ingests articles through Azure Functions and Kafka, runs NLP workers for sentiment analysis and embeddings, clusters related stories, and serves summaries through a FastAPI backend and Next.js frontend. The source code isn't public since the project grew out of Georgia Tech coursework and its Honor Code restricts sharing it - the GitHub repo above has a fuller write-up instead.

Built a fault-tolerant MapReduce system that scales across many concurrent worker nodes, processing large datasets with no job loss under worker/master failures, using heartbeat-driven rescheduling and Azure Blob-backed storage. Same Georgia Tech Honor Code restriction applies here, so the source isn't public.

Specialized in Computing Systems - coursework spanning operating system internals and concurrency, distributed and cloud-native software development, networking protocols and architecture, algorithm design and complexity analysis, and information and network security.

Focused on applied analytics, combining machine learning and data visualization for business decision-making.

A quantitative foundation in economics and finance, combining econometrics and data analysis with management principles.