Global Tech Salary Analysis

Interactive analysis of 200,000 tech job salary records from 10 countries.

Project Overview

This project analyzes global tech compensation trends to answer critical questions about skills demand, experience impact, and working conditions in the tech industry.

Key Questions Answered

1. Which tech skills pay most? - C++ and data science frameworks lead
2. How much does experience matter? - Lead roles pay 2.25x more than Entry level
3. Does company size affect salary? - Minimal impact (Startups ≈ Enterprise)
4. Which countries pay most? - Global salaries relatively balanced
5. Is higher pay linked to satisfaction? - No correlation found
6. Gender pay gap? - Essentially zero across all levels
7. Remote work impact? - Remote pays the same as onsite

Dataset

• Records: 180,000 tech job entries
• Countries: 10 (France, Canada, Japan, Germany, Netherlands, UK, USA, Australia, India, Brazil)
• Dimensions: Skills, salary, experience, company size, remote work, satisfaction, demographics

Key Findings

Skills & Salary

• Top paying skills: C++ ($137k), TensorFlow ($136k), SQL ($136k)
• Most in-demand: React, Docker, JavaScript

Experience Level

• Entry: $86,348 | Mid: $111,300 | Senior: $152,640 | Lead: $194,485
• Clear financial incentive for career advancement

Company Size

• Enterprise: $135,430 | Startup: $135,799 | Mid-size: $135,723 | SME: $135,346
• Company size has almost no impact on salary

Gender Pay Gap

• Entry: -0.2% | Mid: +0.7% | Senior: +0.9% | Lead: -0.0%
• Tech industry shows near-zero gender pay gap

Salary vs Satisfaction

• Correlation: 0.002 (essentially no relationship)
• Higher salaries don't lead to higher job satisfaction

Technologies Used

• Python: Pandas, NumPy, SciPy, Scikit-learn
• Visualization: Matplotlib, Seaborn, Plotly
• Database: SQLite
• Dashboard: Streamlit

Project Structure

├── data/raw/              # Original dataset
├── data/processed/        # Cleaned data
├── src/                   # Python scripts
├── sql/                   # Database queries
├── notebooks/             # Jupyter analysis
├── dashboard/             # Streamlit app
└── reports/               # Findings & charts

How to Run

# Install dependencies
pip install -r requirements.txt

# Load & clean data
python src/data_loading.py
python src/cleaning.py

# Load to database
python src/load_to_sql.py

# Run analysis
python src/advanced_analysis.py
python src/visualizations.py

# View dashboard
streamlit run dashboard/app.py

Visualizations

7 professional charts analyzing skills, experience, company size, geography, remote work, gender pay gap, and salary vs satisfaction.

Limitations

• Salary data in multiple currencies (no cost-of-living adjustment)
• Single snapshot in time (not trends)
• May have sampling bias in data collection

Future Enhancements

• Time-series trends with multi-year data
• Cost-of-living adjustments by country
• Predictive models for salary estimation
• Real-time data updates

GitHub & Links

• GitHub Repository