To access my resume, click on the link in the About
section of this repository (top-right of this screen).
My resume is available as a public version and DXC version (visible only to DXC employees).
Detailing here how this resume is built from one single source code on GitHub, leveraging a CI/CD pipeline, automated testing and our DevOps principles to generate multiple artifacts (HTML, PDF, Word, PPT...).
- Benoit-Bourdin-resume.rmd.j2: my full resume under the R markdown format, making data science beautiful and leveraging the VisualResume library.
- Benoit-Bourdin-slide.md.j2: a short summary of my profile under the Marp markdown presentation format
- resume-data.yml: values of placeholders to be replaced in above two files, to keep any sensible information sensible to DXC hosted in a separated repository.
Source code available here:
- Jenkinsfile
- GitHub actions (in progress)
- We use the vikingco/jinja2cli docker image to run the Jinja2 CLI and easily replace placeholders defined in the
j2
files by values defined in ayml
file.
- We do the following tests:
- linting of the yaml files with yamllint
- linting of the markdown files with markdownlint - configuration here
- spell check using mdspell installed from this Dockerfile and using
.spelling
files from the 2 repositories.
- R is nice for data science.
- To make Data on the DevOps way, we use the rocker/r-rmd docker image (based on Ubuntu) to run R inside a container started by the pipeline.
- To get all the packages we need (including VisualResume), we needed a customized Dockerfile.
- In this case, we use R markdown for rendering, which is using Pandoc for conversion.
- Marp is nice for presentation as code using markdown.
- To make it on the DevOps way, we use the marpteam/marp-cli docker image to run it inside the pipeline.
- The content is pushed to the
gh-pages
branch, then served by GitHub pages.