A LaTeX-based resume template designed for data science and technical roles. This repository contains a customizable resume format with modular content sections and a build pipeline to generate PDF resumes.
Features
- Clean, professional resume template using LaTeX
- Modular content stored in separate section files for easy editing
- Custom styling handled by a dedicated LaTeX style file (
TLCresume.sty) - Python build script automates dependency installation and resume compilation
- PDF output included for quick reference
Tech Stack
- LaTeX (primary language for resume formatting)
- Python (build automation script)
- Make (build automation)
Getting Started
Prerequisites
- LaTeX distribution installed (e.g., TeX Live, MiKTeX)
- Python 3.x
makeutility
Installation and Build
-
Clone the repository:
git clone https://github.com/justin-napolitano/good-resume.git cd good-resume -
(Optional) Install Python dependencies if any are added later:
pip install -r requirements.txt -
Build the resume PDF using the Makefile:
make clean make html -
The generated PDF will be available as
resume.pdf.
Project Structure
/_header.tex # Header file included in main LaTeX document
/deployz/ # Deployment scripts or related files (assumed)
/python-build.py # Python script to automate build and deployment
/README.md # This file
/resume.pdf # Compiled resume output
/resume.tex # Main LaTeX resume source file
/sections/ # Folder containing modular content sections
/TLCresume.sty # Custom LaTeX style file for formatting
- The main LaTeX document (
resume.tex) imports styling fromTLCresume.styand content from thesections/folder. - Contact information and metadata are defined in
resume.tex. - The build process is automated via
python-build.pyandmakecommands.
Future Work / Roadmap
- Add detailed documentation for customizing sections and styles
- Enhance build script to support other output formats (e.g., HTML)
- Integrate CI/CD pipeline for automatic PDF generation on commits
- Expand deployment scripts for hosting updated resumes online
- Include sample data and examples for faster onboarding