About Me
I'm a software engineer currently working at Capital One to develop our web apps and enhance experience for both users and developers.
See my CV
Skills & Experience
Languages & Frameworks
Currently using professionally
- JavaScript/TypeScript
- Node.js
- React.js
- Next.js
- Vite
- Jest/Vitest
- CSS
- styled-components
- Tailwind
- Cypress
- Playwright
Have used professionally/semi-regularly
- Python
- Java
- Fortran
Tools & Infrastructure
- AWS
- Lambda
- CloudFront
- Route 53
- EC2
- Jenkins
- Git
- Docker
- Logging & Monitoring
- Logz.io
- NewRelic
Work History
Capital One
Sept 2021 - Present
Started on the graduate scheme with rotations across various teams working on Java backend services, DevOps, and web applications. Settled into a web team at the end of the rotations and have been working since then on Capital One's customer acquisition web journeys.
Worked on a range of projects such as creating an ASOS-branded version of the QuickCheck journey and migrating on-prem services to the AWS cloud. Drove improvements to developer experience through things such as migrating apps to TypeScript and enhancing our documentation.
Main technologies used in my current role are JavaScript/TypeScript, Node.js, React, Next.js, CSS (styled-components), AWS (Lambda, CloudFront, S3), Jenkins for CI/CD, Git + GitHub, Cypress.
Met Office
July 2019 - June 2020
Completed a placement year between my 2nd and 3rd year of university working as a Scientific Software Engineer at the Met Office. I started the placement working in Weather Science IT on LFRic - the Met Office's next generation weather model. This involved using Python and Fortran to efficiently input thousands of weather data fields into the model.
I also got the opportunity to work on a project in the Met Office's Innovation Lab to enable scientists to easily integrate machine learning models into the weather model. This involved taking Tensorflow models built with Python, compiling them down, and creating an interface with C++/Fortran to get input/output from the weather model.
Education
University of York
2017 - 2021
I wrote my final-year dissertation on the efficacy of transfer learning neural networks for categorising physical activities measured by wearable devices (smartphones and watches). Used Python with Tensorflow to build, train, and test this model on the WISDM dataset.
Hobbies & Interests
- Software Development (obviously!)
- Rock Climbing
- Music - playing guitar/bass/drums
- Motorsport - viewing and taking part in real & virtual racing
- Cats - too many to count currently in the house