I am a software engineer at LinkedIn with over 10 years of experience and I love writing easily extensible, testable, and above all, readable code! I am most excited about building cool end-to-end products that need to operate at scale.
San Francisco Bay Area
Leading 15+ engineers with the development, early preview, and launch of LinkedIn Business Suite – a new premium subscription designed to help small business owners sell, market, and hire on LinkedIn
Implemented AI generated hiring and sales messaging that personalizes outreach to candidates and sales prospects
Expanding non-English language support for the “Rewrite Post with AI” feature
Led cross-functional collaboration with Oracle HRM, LinkedIn leadership, and the Job Ingestion Engineering team to build outbound integrations, unlocking 2M+ additional jobs for LinkedIn and making the Easy Apply experience available to large enterprise customers by rearchitecting the tech stack to function like a platform within LinkedIn
Managed a global engineering team of 17 engineers by organizing sprint stories, conducting code and design reviews, and driving knowledge transfer sessions to ensure timely delivery of key milestones
Spearheaded the transition of Apply Integrations charter to an offshore team by conducting deep-dive knowledge transfer sessions, ensuring sustained program success and minimal disruption to core services during the transition
2021 — 2023
San Francisco, CA
Architected and built “Apply Connect Lite”, enabling Applicant Tracking System partners to continue sending job data via XML while seamlessly integrating real-time screening questions from LinkedIn APIs, ultimately growing the number of Easy Apply jobs from 50k to 380k and the number of weekly job applications delivered from 350K to 3.75M in 2.5 years
San Francisco, California, United States
While at Amazon, I have had the opportunity to operate under multiple team charters with a group of talented peers to solve some really interesting problems.
I am currently part of the Alexa Engagement Incubation team, whose objective is to rapidly prototype experiences with a goal to increase customer engagement with Alexa. On this team I've had the opportunity to design, prototype, and implement an Alexa Skill that powers an essential feature for the next generation of Alexa devices. I also had the opportunity to prototype two separate mobile experiences (one built with React Native and TypeScript and the other being a native Android application).
On the Softlines Discovery Science team, our objective was to support Machine Learning scientists in the improvement of search results for clothing, shoes, jewelry, and luggage on amazon.com. While on this team I designed and implemented a series of scheduled Spark jobs that aggregates customer search queries from around the world and extracts various features from the respective search results. I then ingested this data (50M+ rows) into AWS QuickSight and built a dashboard that gave leadership and stakeholders visibility into our customers top search queries while allowing our team to monitor, investigate, and quickly troubleshoot defects in search result quality.
While on the Amusement Team, whose objective was to deliver Original Content (Songs, Jokes, Poems, Easter-Eggs, etc...) and enhance Alexa’s unique personality, I had the opportunity to re-implement the “tell me a joke” experience (1.75M+ requests per day) which improved latency by ~85% while decreasing server costs by ~70%.
While on the Answer Gathering Team, whose objective was to source answers for all subjective questions asked to Alexa i.e. “What’s your favorite ____?”, I designed and built a real time system on AWS Lambda and Elasticsearch to analyze, identify, and prevent low quality answers from being sourced.
2014 — 2017
Austin, Texas Area
I worked closely with Product Developers to fix and enhance various applications and tools managed by the Portfolio Metadata Team which is primarily responsible for the Portfolio List Manager Application within the FactSet Workstation.
Key Achievements:
Migrated five metadata parsers from OpenVMS to Linux and also implemented a cross platform regression test suite for them
Designed, wrote, and conducted a distributed stress test on an in-house file system and successfully identified a major bottleneck in performance which ultimately led to upper management seeking an alternate solution to cross platform data storage
Helped onboard a million-dollar client by taking full ownership of an Application Level Security Parser and performing the necessary enhancements needed to allow users to set read/write permissions on both files and directories as required by the client
Enhanced Data Download (Screening via Excel) to allow clients to quickly identify Accounts that match specific metadata search parameters
2014 — 2015
Used AngularJS to design and build a mobile website through which users could place pick up orders for food at local restaurants
Implemented a NodeJS RESTful API to serve dynamic data about the various menu items from restaurants to the front-end
Education
2014
The University of Texas at Austin
Bachelor’s Degree
2014
2018 — 2019
Amazon Machine Learning University
2018 — 2019