This project utilizes a comprehensive dataset of global data job postings. To ensure complete reproducibility, all necessary files have been included in the repository.
The repository contains two main folders:
- csv_files: Contains the raw dataset files
- sql_load: Contains SQL scripts for table creation and data loading
- PostgreSQL (recommended for full compatibility)
- Excel (For visualizations)
- Note: While other RDBMS might work due to similar SQL syntax, PostgreSQL is preferred for guaranteed functionality
- Download the ZIP file from GitHub
- Extract the contents to your desired location
- Execute the SQL scripts located in the sql_load folder
If you encounter permission-related issues during execution, refer to the detailed troubleshooting steps provided as comments within the SQL files.
To effectively understand and work with the queries in this project, you should be familiar with the following SQL concepts:
- Basic SQL Statements
- SQL Joins
- Unions
- Subqueries
- Common Table Expressions (CTEs)
- Window Functions
- And more
This project provides a collection of SQL queries designed to offer unique insights for job seekers in the field of data analytics. The queries enable users to explore the job market by analyzing key aspects such as top-paying jobs, high-demand skills, top-paying technical skills, salary trends, and more. Additionally, visualizations have been included to enhance understanding and make the data more accessible.
This section highlights key insights derived from the SQL queries, each supported by a visualization for better understanding. While not every possible permutation and combination is covered here, I highly encourage exploring the SQL files directly for a more comprehensive view. [The Excel worksheet used for visualizations has been included for your reference.]
The role of a Data Analyst not only offers competitive compensation but also remains consistently in demand.
While specialized skills often attract higher salaries, they come with trade-offs, such as limited job availability.
Tools and languages like SQL, Excel, Python, Tableau, and Power BI continue to lead as the most in-demand skills in the industry.
Similar to previous observations, niche technical skills consistently result in higher pay scales for professionals.
Despite the complexity of the data, the conclusion remains steady: niche skills command a premium, while universally required skills like SQL and Excel offer solid but comparatively lower pay.
Job listings see a noticeable spike mid-year before tapering off toward the end of the year.
How does each company's data analyst salary compare to the industry average, and which companies consistently pay above the market rate?
Leading tech companies like Mantys, OpenAI, and Anthropic consistently offer salaries well above market standards, making them stand out as top-paying employers.
What is the salary premium for each skill compared to the running average of all data analyst positions?
Across all visualizations, the trend is clear—niche skills lead to significantly higher salaries when compared to broader, in-demand technical skills.