July 2016 - 2023
Engineering Manager (2019-2023)
- Led complex projects for scalable React front-end applications, data systems, and back-end microservices to our 300M monthly unique visitors across multiple engineering groups.
- Expanded strategy for performance dashboards and observability on our client-side systems.
- Led a team of 9 engineers focusing on findability, video, and experimentation. Ensured teams were able to deliver high-quality features to meet business objectives rapidly.
- Partnered with product leaders to align short-term goals while working with the VP of Engineering to build the correct infrastructure to support the business in the long term.
- Executed a strategy to unify the front-end platform with consistent tooling and technology.
- Hired global talent across multiple departments.
- Developed top engineers into leadership roles by providing individualized mentorship and guidance tailored to the specific motivations and goals of each engineer.
Staff Engineer (2016-2019)
- Technical lead for a browser experimentation platform, client-side data management toolkit for personalization, and video platform.
- Built a fast-to-market editorial platform and Fandom’s React replacement of MediaWiki.
- Unified client-side performance and analytics systems.
- Led technical code-sharing groups for new technology and engineering patterns.
- Worked closely with international engineering, design, and product partners.
Fireman Creative | Full Stack Software Engineer
July 2015 - July 2016
- Independently led several completed web projects from design to production.
- Collaborated with clients and graphic designers to craft a consistent look and feel of web applications.
- Generated monthly SEO and analytical progress reports to track progress.
- Created a new container-based deployment process that greatly sped up development flows.
- Implemented automated alerts and scripts to restart crashed applications and servers.
Pittsburgh Quantum Repository Group | Full Stack Software Engineer
January 2015 – January 2016
- Built python-powered front-end application to display the largest molecule database.
- Utilized WebGL technology to create 3D visualizations of molecules.
- Built and maintained a MongoDB server.
Pittsburgh Genetics | Full Stack Software Engineer
October 2014 – January 2016
- Built a genetic population simulator web application used by students and professors across the US.
- Teaching tool used across multiple universities.
- Utilized multithreading web workers to speed up simultaneous math calculations.
- Utilized multi-threading web workers to speed up simultaneous simulations.
Independent Consultant | Full Stack Software Engineer
January 2010 – Present
- (2023) Actively working with Howard Hughes Medical Institute building tools for expanding understanding of genetics.
- Worked with professional baseball teams, writers, students, and professors to build web applications.
- Built a CMS for San Francisco Giants affiliate team player management.
- Built tools to automate audio uploads for a blogger.
- Advised faculty and staff at the University of Scranton as a Technical Consultant.
- Migrated years of content from custom websites to a CMS.
- Created a proposal for which Learning Management System the University should use.
|Backend||Node.js, Java (Gradle, Guice, Jackson, Jersey, Jooq, Liquibase), Python, PHP (Laravel, Composer, WP), RabbitMQ, Memcached, and Algolia.|
|Analytics||Athena, Google Analytics, Mode, and Qlik.|
|Data/Database||MySQL, MongoDB, GraphQL, and Neo4J.|
|Ops||Kubernetes, AWS, GCP, Jenkins, Travis CI, Kibana, Grafana, Prometheus, and Varnish Control Language.|
|Data Engineering||SKLearn, Tensorflow, Pandas, and Jupyter Notebook.|
|Design||Figma, Zeplin, Sketch, Canva, LucidChart.|
|Miscellaneous||Agile, Jira, Photoshop, Lightroom, and Premiere Pro.|
|Personal Interests||Photography, woodworking, boardgames, disc golf, and baseball.|
MLB Stats Project
Baseball has been one of the strongest influences on my life. I grew up watching it nearly every night with my Dad and playing all year long (in some form) through high school. I love the all of the nuances in the sport. As a baseball evangelical, I spend every free moment taking in baseball. From the front office economics to the 9th inning ejection, I simply can’t get enough. This derangement (I wanted to say passion but my girlfriend disagreed), has led me down the analytics rabbit hole that each team’s front office is competing against one another to uncover some novel stat ignored by moneyball.
pitchFX Data Analysis
Major league baseball has been releasing data of every single pitch thrown for the last 5+ years. This dataset doesn't just contain the velocity and angle but includes things like launch angle, vertical break, horizontal break, release rotation, height the pitch was released at, exit velocity and much more.
I've been looking for a project to utilized machine learning techniques and this dataset was begging to be analyzed.
I had used some basic machine learning tools in the past such as linear support vector machines, and k-nearest neighbor's. But I really want to try to our neural networks. My initial findings indicated that LSVM outperformed neural networks in speed and accuracy. I'm skeptical of these results and wonder if I have a flaw in the setup of my neural network
I started building a UI that allows the user to position a pitch in a virtual strike zone, give it velocity, breaking, and angle variables and the app will determine the likelihood of a hit.
Political Article Analyzer
I find myself more and more frustrated in my search for fair news. The internet lends itself to creating "click-bait" like article titles which isn't great in the search for truth. I started creating a tool that can detect the political leaning of individual articles through natural language processing techniques. Currently, I am attempting to first analyze text and determine the publisher by computing term frequency–inverse document frequency among other things. Next, I would like to investigate sentiment analysis of images. I believe each news outlet choose less flattering images of their "opponent" and I'm curious if the data bears that out.
I also discovered there is an API that attempts to do this: AllSides. I find their ratings quite fair and will use that as a sample set to compare my results.
Some of the code lives in this repo
There is a small front end app I'm using to collect data: Left-Right-Center Data Collection. If you find an article very biased you can copy/paste the link and rate it.