* Developed a system automating the deployment of review instances of the Betterworks application

stack on a Kubernetes cluster based on GitHub pull requests.

* Worked in a small team to develop a multi-account AWS setup using Terraform in preparation for

a migration from Heroku to AWS + Kubernetes.

* Advised development teams working with legacy features in order to help them navigate related

tech debt and potential pitfalls.

* Debugged and triaged live production issues for top customers.

* Refined and formalized our release and deployment process with an emphasis on repeatability and

minimizing the surface area for mistakes.

* Responsible for the full stack, end to end development of features (AngularJS/React on the front end,

Django on the back end), with a focus on third party integrations, the BetterWorks public API,

and plugins (Chrome extension, Outlook add-in).

* Developed a framework for integration development with third party APIs. This framework takes a YAML

formatted manifest that includes descriptions for authentication methods, API transformations, and UI

components, allowing for a fully end-to-end implementation with minimal code.

* Developed scripts to automate the release and deployment of integration related services that span

multiple code bases across multiple stages of development.

Designed and implemented an automated test system for PCLC’s second generation of prototype boards

using multiple Raspberry Pis at each board coordinated over a network through Pyro (Python Remote

Objects).

(Django), and provided an API allowing test locations to automatically report data (Tastypie).

Open Source Contribution Highlights

Document—initial support for form resetting, indeterminate check boxes, and the <​textarea> element

(Rust), additionally providing convenience features and updates to the project’s build system (Python).

Designed and implemented software for a project to determine the feasibility of using task plane mounted

illuminance sensors for controlling ambient lighting units in a daylight harvesting system using a

self-establishing network of Raspberry Pi’s (Python).

adjust light levels.

levels and sensor calibration.