Dotfiles – Data Engineering & AI Platform Setup

Purpose

Version-controlled collection of personal dotfiles and environment configurations designed for Data Engineering, Data Platform Engineering, Data Architecture, and AI-oriented workflows.

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Overview

This repo contains configurations, scripts, and conventions that define a reproducible, efficient, and scalable development environment.

It is designed to:

• Standardize local environments across machines
• Automation
• Improve productivity
• Promote consistency and best practices
• Showcase engineering mindset and tooling choices

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Philosophy

This repository reflects a few core principles:

• Reproducibility as a foundation
• Automation over manual work
• Clarity over cleverness
• Composability over monolithic setups
• Explicit configuration over implicit behavior

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Repository Structure

dotfiles/
├── data/                 # Data-related utilities, templates, configs
├── git/                  # Git configuration
│   ├── .gitconfig
│   ├── .gitignore
│   ├── .gitignore_global
│   └── .gitmessage
├── python/               # Python environment setup
│   └── python_env.sh
├── shell/                # Shell configuration (Zsh-based)
│   ├── .zshrc
│   ├── aliases.sh
│   └── env.sh
├── install.sh            # Bootstrap installation script
└── README.md

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Features

• Modular and extensible structure
• Reproducible environment setup
• Python environment bootstrapping
• Thoughtful shell aliases and environment variables
• Opinionated Git configuration for professional workflows
• Optimized for productivity in data-intensive environments

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Installation

Prerequisites

• Unix-based OS (Linux/macOS)
• zsh installed
• git installed

Setup

git clone https://github.com/ghakimian/dotfiles.git
cd dotfiles
chmod +x install.sh
./install.sh

⚠️ Review the install.sh script before running it to understand what changes will be applied to your system.

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Python Environment

The python/python_env.sh script is intended to:

• Standardize Python setup
• Configure virtual environments
• Prepare dependencies for data and AI workflows

You can customize it depending on your preferred tooling:

• pyenv
• venv
• poetry
• conda

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Shell Configuration

Zsh is used as the primary shell.

Key components:

• .zshrc: Main configuration file
• aliases.sh: Productivity shortcuts
• env.sh: Environment variables and paths

Design principles:

• Minimal but powerful
• Readable and maintainable
• Performance-conscious

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Git Configuration

Includes:

• Global ignore rules
• Commit message template (.gitmessage)
• Opinionated defaults for professional workflows

Goals:

• Enforce clean commit history
• Improve collaboration standards
• Reduce noise in repositories

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Data Folder

The /data directory is a placeholder for:

• Templates
• Schemas
• Sample datasets
• Configuration files for data tools

This will evolve as part of ongoing experimentation and professional use.

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Security Notice

This is a public repository.

Never commit:

• Secrets (API keys, tokens, credentials)
• Private certificates
• Sensitive environment variables

Use .env files and secret managers where appropriate.

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Roadmap

• Add support for containerized environments (Docker)
• Integrate with cloud CLI tools (AWS, GCP, Azure)
• Expand Python environment management (Poetry/UV)
• Add data tooling configs (dbt, Airflow, Spark)
• AI/ML tooling presets (Jupyter, MLflow, etc.)

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Contributing

This is a personal repository, but suggestions and discussions are welcome.

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License

MIT License

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Author

Gregorio Hakimian

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Final Note

This repository is a living artifact of how I think about:

• Developer experience
• Data platforms
• Scalable environments for AI-driven systems

Feel free to explore, fork, and adapt it to your own workflow.