Job Analysis SQL Project

📌 Introduction

This project focuses on analyzing job market data using SQL, specifically targeting remote job postings. The main objective is to identify the most in-demand skills and understand how companies list jobs requiring those skills. This analysis can provide insight into job market trends and which technical skills are currently valuable.

📚 Background

With the growth of remote work, understanding the dynamics of job postings has become important for job seekers and employers. The dataset used in this project includes job listings, required skills, company information, and other job-related attributes. By using SQL, I can extract meaningful patterns and trends from this data to better understand the remote job landscape.

🛠 Tools I Used

• PostgreSQL – For creating and managing the database
• pgAdmin – To run SQL queries and interact with the database
• GitHub – To store and document the project
• SQL – For data analysis (joins, aggregation, subqueries, CTEs)

📊 The Analysis

The main tasks I performed include:

• Top paying Data Engineer Jobs
• Displaying what skills are reqiured for Data Engineer in a job market
• Showing what are the most demanded skills for Data Engineer
• Exploring what are the top-paying skills for Data Engineer
• Figuring out what are the most optimal skills to learn

Example Query: Top Demanded Skills for Data Engineer

SELECT 
    skills,
    COUNT(skills_job_dim.job_id) AS demand_count
FROM job_postings_fact
INNER JOIN skills_job_dim ON job_postings_fact.job_id = skills_job_dim.job_id
INNER JOIN skills_dim ON skills_job_dim.skill_id = skills_dim.skill_id
WHERE
    job_title_short = 'Data Engineer' AND
    job_work_from_home = TRUE
GROUP BY
    skills
ORDER BY
    demand_count DESC
LIMIT 5

🔎 Here's the result of top 5 most demanded Data Engineer skills in 2023:

🔢 Rank	💼 Skill	📈 Job Listings Requiring Skill
1	SQL	14,213
2	Python	13,893
3	AWS	8,570
4	Azure	6,997
5	Spark	6,612

These numbers were extracted from job postings and reflect the most common technical requirements for Data Engineering roles in 2023. SQL and Python continue to dominate the field, while cloud platforms like AWS and Azure, along with big data tools like Spark, are also highly valued by employers.

🧠 What I Learned

• How to join multiple tables in a normalized database
• The difference between subqueries and CTEs (Common Table Expressions)
• How to use GROUP BY, ORDER BY, and LIMIT to analyze data
• How to filter and sort data to find meaningful insights
• The importance of using descriptive aliases and organizing queries clearly

✅ Conclusions

This project helped me strengthen my SQL skills and better understand how to analyze data from a real-world dataset. I learned how to extract key insights about the remote job market, especially which skills are most valuable to employers. These techniques are applicable to many areas of data analysis and business intelligence.

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Created by Ivan Shvets
GitHub: ivanswetz