SDE 2 for Microsoft's Data & AI Platform Team, developing an internal platform toolkit designed to make entire lifecycle of data insight creation & management more scaleable and efficient.

Improved platform security and functionality by removing plaintext secrets, implementing UAMI support, enhancing Logic Apps with renewal email functionality to reduce processing time, and supporting the Access Management Module to align with v2 product goals and system onboarding.

SDE 2 for Microsoft's Data Insights & Analysis Team, developing real-time processing infrastructure, POC implementations, and robust monitoring solutions for pipelines. Promoted within the same title band, reflecting advanced responsibility, leadership, and expertise.

Implemented configuration-based paging for four production APIs, reducing response time by 59%; automated 80% of manual onboarding for real-time data sources using Azure Durable Functions, saving 960 person-hours; and enhanced monitoring coverage by ~30% by building latency, throughput, and failure detection solutions with PowerBI, ADX, and Spark Applications.

Lead developer of a platform automating ETL pipeline applications by architecting the framework, library, and API, implementing key processors like deduplication and SQL Join, providing UI functionality, and integrating deployment workflows on Databricks, AWS Glue, and Spark-Standalone Cluster. Designed event logging to InfluxDB, MariaDB, and Amazon SES for dashboard views and notifications.

Authored a white paper, presented demonstrations to clients, created sample workflows using DataMorph, and recorded informational videos to showcase the platform.

Developed a proprietary ML framework, achieving 97% accuracy with an entity resolution model and deploying the integrated solution in Scala.

Consulted as a Kwartile Big Data Specialist with Nordstrom's Credit Team, designing high-level and detailed architecture documentation for scalable software solutions, collaborating with business analysts on event sourcing models, developing a reference application with Kafka, Java, and Spring Boot, deploying a POC with Docker/Kubernetes, setting up system monitoring with Data Dog and New Relic, and leading knowledge transfer sessions on big data practices and solution architecture.

Researched under the guidance of Professor Samuel King in the security department. With the aim to utilize machine learning to better identification of unique users with consumer grade EEG devices, in order to improve authentication security.