📊 SQL Project: Data Job Market Analysis

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Introduction

This project focuses on analyzing the Data Analyst job market using SQL to uncover insights related to salary trends, skill demand, and optimal skill combinations.
The analysis answers practical, career-focused questions using real-world job posting data.

All insights are derived from SQL queries executed on a PostgreSQL database, with results visualized for clearer interpretation.

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Background

With the rapid rise of data-driven decision-making, Data Analyst roles have become highly competitive. Job seekers often struggle to understand:

• Which roles offer the highest salaries
• Which skills are truly in demand
• How skill demand correlates with compensation

This project addresses these questions by analyzing job postings, salary information, company data, and required skills using SQL.

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Tools I Used

• PostgreSQL (SQL)

  • Joins, aggregations, filtering
  • CTEs and subqueries
  • Window functions (RANK)
  • Date & time zone conversions

• Python

  • pandas for handling query outputs
  • matplotlib for generating visualizations

• Git & GitHub

  • Version control
  • Project documentation

📌 Large datasets, generated results, and visualizations are excluded from the repository for cleanliness and efficiency.

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📁 Project Structure & File Organization

📂 Sql_Project_Data_job_Analysis

└── 📁.vscode

└── 🛠️settings.json

└── 📁advanced_sql

├── ⛁ Case_Expressions.sql
├── ⛁ Database_Creation(sql_course).sql
├── ⛁ Date_Functions.sql
├── ⛁ Monthwise_job_tables.sql
├── ⛁ Sample TABLE jobs_applied.sql
├── ⛁ Subqueries_&_CTEs.sql
└── ⛁ Union_Operators.sql

└── 📁csv_files

├── 👻.DS_Store
├── 🧾company_dim.csv
├── 🧾job_postings_fact.csv
├── 🧾skills_dim.csv
└── 🧾skills_job_dim.csv

└── 📁images

├── 🖼️ 1_top_paying_jobs.png
├── 🖼️ 2_top_paying_jobs_skills.png
├── 🖼️ 3_top_demanded_skills.png
├── 🖼️ 4_top_skills_by_salary.png
├── 🖼️ 5_top_optimal_skills.png
└── 🖼️ 6_latest_jobs.png

└── 📁project_files_sql

├── ⛁ 1_top_paying_jobs.sql
├── ⛁ 2_top_paying_job's_skills.sql
├── ⛁ 3_top_demanded_skills.sql
├── ⛁ 4_top_skills_by_salary.sql
├── ⛁ 5_top_optimal_skills.sql
└── ⛁ 6_latest_jobs.sql

└── 📁results_csv

├── 🧾latest_jobs.csv
├── 🧾top_optimal_skills.csv
├── 🧾top_pay_jobs_skills.csv
├── 🧾top_paying_jobs.csv
├── 🧾top_salary_skills.csv
└── 🧾top_skills_DA.csv

└── 📁sql_load

├── ⛁ 1_create_database.sql
├── ⛁ 2_create_tables.sql
└── ⛁ 3_modify_tables.sql

└── 🚫.gitignore

└── 🐍generate_vizualizations.py

└── 📝README.md

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📌 Significance:

• .vscode/: VS Code + SQLTools configuration (gitignored)
• advanced_sql/: SQL learning & experimentation (7 fundamental files)
• csv_files/: 129MB raw csv files of jobs_dataset , to be loaded into empty tables created in sql_load/'s 2_create_tables.sql , using 3_modify_tables.sql (gitignored - main 123MB job_postings_fact.csv)
• images/: Generated PNG visualizations from analysis (gitignored)
• project_files_sql/: All 6 core queries which was used to perform the data analysis on our jobs_dataset, on the basis of the problem statements mentioned for each query (main deliverables)
• results_csv/: Raw SQL query outputs (gitignored)
• sql_load/: Production-ready ETL pipeline (database setup + data loading)
• .gitignore: Smartly excludes 129MB+ data (1.5MB GitHub repo)
• generate_visualizations.py: Python script creating charts (gitignored)

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The Analysis

📂 Working in project_files_sql/ folder - contains All 6 core SQL queries which is used to perform the data analysis on our jobs_dataset, on the basis of the problem statements mentioned for each query (in the --comments) :

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1️⃣ Top Paying Remote Data Analyst Jobs

SQL File: 1_top_paying_jobs.sql

Problem Explanation:
This query identifies the top 10 highest-paying Data Analyst jobs that are available remotely and have non-null salary information.
The objective is to evaluate whether high compensation is limited to on-site roles or achievable in remote positions.

Result Analysis:
The results show that several remote Data Analyst roles offer salaries exceeding $150K–$200K annually.
This confirms that location independence does not restrict earning potential and that high-paying opportunities exist across a range of companies.

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2️⃣ Skills Required for Top Paying Jobs

SQL File: 2_top_paying_job's_skills.sql

Problem Explanation:
This analysis determines which skills are most commonly required in the highest-paying Data Analyst roles.

Result Analysis:
SQL appears in nearly all top-paying jobs, reinforcing its role as a foundational skill.
Python and Tableau are also highly prevalent, highlighting the importance of programming and data visualization in high-compensation roles.

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3️⃣ Most In-Demand Data Analyst Skills

SQL File: 3_top_demanded_skills.sql

Problem Explanation:
This query ranks skills by the number of Data Analyst job postings that require them, revealing overall market demand.

Result Analysis:
SQL and Excel dominate demand, confirming they are baseline requirements for most roles.
Python, Tableau, and Power BI follow closely, emphasizing the importance of analytical and visualization skills.

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4️⃣ Top Paying Skills by Average Salary

SQL File: 4_top_skills_by_salary.sql

Problem Explanation:
This analysis identifies skills associated with the highest average salaries, regardless of how frequently they appear in job postings.

Result Analysis:
Highly specialized and infrastructure-related skills command premium pay.
These skills are less common but significantly increase earning potential, often reflecting scarcity and advanced expertise.

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5️⃣ Most Optimal Skills (Demand vs Salary)

SQL File: 5_top_optimal_skills.sql

Problem Explanation:
This query combines job demand and average salary to identify the most optimal skills to learn.

Result Analysis:
SQL offers the strongest balance between demand and compensation.
Python and Tableau provide strong salary upside, while Excel—though highly demanded—offers less differentiation in pay.

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6️⃣ Latest Data Analyst Job Postings

SQL File: 6_latest_jobs.sql

Problem Explanation:
This query retrieves the most recent Data Analyst job postings after February 2023 and converts posting times from UTC to U.S. Eastern Time.

Result Analysis:
The latest postings show continued hiring activity across regions and remote roles.
Salary ranges vary widely, reflecting differences in role scope, seniority, and company expectations.

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What I Learned

• How to write real-world analytical SQL queries
• Practical use of CTEs, subqueries, and window functions
• How to translate raw SQL results into actionable insights
• How demand and salary do not always correlate directly
• How to document a complete data analysis project professionally

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Conclusions

This project demonstrates that:

• SQL is the most critical skill for Data Analysts
• Combining SQL with Python/R and visualization tools maximizes career potential
• Specialized skills unlock higher salary tiers
• Remote roles can offer compensation comparable to on-site positions

Overall, this project shows how SQL-driven analysis can provide meaningful insights into the data job market and support informed, data-driven career decisions.