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Languages: Kotlin, Java, Ruby, Python

Technologies: Rails, Dropwizard, PostgreSQL, PrestoSQL, AWS, Terraform, OpenAPI, Locust

• Developed control plane services in Ruby w/ Ruby on Rails and Kotlin w/ Dropwizard, which managed configuration entities for data transformations and flows within our Customer Data Platform (CDP). Deployed these services on AWS with Terraform.
• Verified system performance and checked for data model consistency issues by designing and running load tests with Locust.
• Enabled greater insights into customer usage of our CDP, along with performance issues, by pushing for and developing analysis tooling for our internal query DSL.
  • Gave developers access to customer query data by building out a data pipeline to generate a sanitized query corpus.
  • Developed accurate encodings of our DSL in Kotlin which served as the foundations for our analysis tooling, and later on a new line of business.
• Provided customers with more fine-grained control over their configurations as the technical owner for a cross-functional initiative to extend our internal permissions system.
  • Added a new RBAC domain layer and interpreter to our existing Rails application with sufficient expressiveness to encode our existing model and the extensions required.
  • Ensured a smooth deployment (and customer experience) by coordinating feature release with feature flags.
  • Led a Junior Software Developer, providing mentorship and support.
• Introduced new (and sometimes old) technologies, techniques and best practices to our team by running a regular paper reading group.
• Handled incidents as part of a regular on-call rotation and ran incident retrospectives.
  • Improved our operations by updating runbooks, tweaking dashboards, introducing additional monitoring and actively mentoring other developers during incidents.
• Helped grow our Vancouver team from 3 to 10 developers
  • Interviewed potential candidates for our team.
  • Reduced onboarding times from months to weeks by performing a deep clean of our onboarding material and updating our service documentation.

Languages: Typescript, F#

Technologies: React, Redux, GraphQL, Webpack, Jenkins

• In order to ship features for our users, became the sole frontend developer at the company and onboarded myself onto a pre-existing React Typescript project.
• Enabled faster feature iteration, and deployments, by implementing CI/CD pipelines for our frontend and backend codebases based on a git feature branch workflow.

Languages: Scala, Python, C++, C#

Technologies: Docker, PostgreSQL, RabbitMQ, Apache Thrift, Play Framework, Scala.js

• Designed APIs for our iOS and Android applications using Apache Thrift RPC.
• Implemented backend features using the Play Framework, PostgreSQL and RabbitMQ.
• Implemented administrative UIs using Scala.js.
• Performed performance profiling of C++ code used for Computer Vision tasks and implemented optimizations.

Post-Acquisition

• Enabled data integration between Bitlit Media and Rakuten Kobo by developing an interconnect over RabbitMQ.
• Containerized development dependencies to allow for development in both Linux and Windows environments.

Languages: Java

• Implemented custom weather charts, along with associated backend logic to feed them, for the MSN Weather Android application.

Languages: Java, JavaScript

• Worked on the Kindle Whispercast team to develop customer facing features using JavaScript, jQuery and the Spring Framework.

Languages: Python

• Created device wrappers in Python to allow for new equipment, such as voltage sensors, pressure and cameras, to be incorporated into an existing experimental framework.

Languages: PHP

• Tested applications on iOS and Android devices.
• Created custom data feeds and services using PHP for testing purposes.

Languages: Racket

• TA for CPSC 110 [Computation, Programs, and Programming], the introductory Computer Science course at UBC.
• Guided groups of ~20 students through weekly computer science labs.
• Balanced 1-on-1 time with students with the needs of the whole lab group.

Community

Run twice monthly discussion sessions on Computer Science papers for groups of 4-10 people.