📊 Introduction

This project dives into the data analyst job market using SQL as the primary tool of investigation.
The aim is to uncover:

• 💰 The highest-paying jobs for data analysts
• 🔥 The most in-demand skills in the industry
• 📈 The skills that strike a balance between being in high demand and offering high salaries

By writing carefully structured SQL queries, I transformed raw job posting data into actionable insights. These findings can help guide career development decisions, inform skill-building strategies, and provide a clearer picture of the evolving landscape for data analysts.

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🎯 Background

The motivation behind this project came from my own curiosity:

If I were preparing for a career in data analytics, what jobs and skills should I target to maximize both employability and salary potential?

To answer that, I focused on five key questions:

1. What are the top-paying data analyst jobs?
2. What skills are required for these top-paying jobs?
3. Which skills are most in demand overall?
4. Which skills are associated with higher salaries?
5. What are the most “optimal” skills — those that combine high demand with high pay?

The dataset used for this project was provided by Luke Barousse’s SQL Course. It included:

• Job postings with details on title, company, location, salary, and posting date
• Skills associated with each job (e.g., SQL, Python, Tableau)
• Metadata linking jobs ↔ skills ↔ companies

This data gave me a unique opportunity to replicate a real-world analytics workflow: asking business questions → querying a database → analyzing results → presenting findings.

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🛠️ Tools I Used

This project combined SQL analysis with modern development tools:

• SQL – the backbone of the analysis, used to filter, aggregate, and join tables.
• PostgreSQL – database engine managing the job postings dataset.
• Visual Studio Code (VS Code) – my development environment for writing and testing SQL queries.
• Git & GitHub – version control and collaboration, used to track query development and host this project.

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🔎 The Analysis

1️⃣ Top Paying Data Analyst Jobs

Question: What are the 10 highest-paying remote data analyst roles?

Approach:

• Filtered for job postings with the title Data Analyst, a specified salary, and remote availability.
• Sorted results by average yearly salary.
• Limited results to the top 10.

Findings:

• Salaries ranged from $184,000 → $650,000.
• Employers included major firms like SmartAsset, Meta, and AT&T.
• Job titles varied (e.g., Data Analyst, Senior Analyst, Director of Analytics), showing a wide spectrum of responsibilities under the “Data Analyst” label.

Figure 1: Salary distribution for the top 10 highest-paying remote Data Analyst roles in 2023.

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2️⃣ Skills for Top Paying Jobs

Question: What skills are required for the highest-paying jobs?

Approach:

• Created a CTE of the top 10 paying jobs (from query 1).
• Joined this with the skills tables to list technical skills for each job.

Findings:

• SQL appeared in 8 out of 10 roles → the single most critical skill.
• Python appeared in 7 jobs, showing the industry’s strong demand for programming.
• Tableau (6 jobs) emphasized the importance of data visualization.
• Other skills included R, Snowflake, Pandas, and Excel, highlighting a mix of database, programming, and reporting tools.

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3️⃣ In-Demand Skills

Question: Which skills are requested most frequently overall?

Approach:

• Counted skill occurrences across all job postings for Data Analyst.
• Grouped results by skill name.
• Ordered by frequency.

Findings:
The top 5 in-demand skills were:

Skill	Demand Count
SQL	7,291
Excel	4,611
Python	4,330
Tableau	3,745
Power BI	2,609

This reveals that SQL and Excel remain foundational, while Python and visualization tools (Tableau, Power BI) are increasingly required for analytics storytelling.

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4️⃣ Skills Based on Salary

Question: Which skills are associated with higher average salaries?

Approach:

• Calculated the average yearly salary for jobs requiring each skill.
• Rounded results for readability.
• Ranked skills by salary.

Findings:

• Big Data & ML Tools (PySpark, Couchbase, DataRobot, Jupyter) ranked at the top.
• Cloud & Infrastructure Skills (Databricks, GCP, Elasticsearch, Kubernetes) commanded higher salaries.
• Python libraries (Pandas, NumPy) also showed high value.

This indicates that specialized, technical skills in data engineering and ML pipelines can significantly boost earning potential.

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5️⃣ Most Optimal Skills

Question: Which skills combine high demand and high salaries?

Approach:

• Built two CTEs:
  • skills_demand → demand count per skill
  • average_salary → average salary per skill
• Joined them to align demand with salary.
• Ranked by both demand and salary.

Findings:

• SQL: High demand and competitive pay → foundational and irreplaceable.
• Python & R: Widely demanded and decently paid.
• Cloud & Big Data skills (Snowflake, AWS, Azure, BigQuery): Provide the “salary boost” edge.
• Visualization Tools (Tableau, Looker, Power BI): Required for practical business use cases.

These results show that the most optimal skill set balances:

• Core fundamentals (SQL, Excel)
• Programming (Python, R)
• Visualization (Tableau, Power BI)
• Cloud/Big Data (Snowflake, PySpark, AWS)

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📚 What I Learned

Through this project, I gained both technical SQL skills and practical analytics experience:

• 🧩 Complex Query Crafting – mastered CTEs, multiple joins, and filtering conditions.
• 📊 Aggregation & Summaries – used COUNT, AVG, ROUND to derive insights from job postings.
• 💡 Analytical Thinking – converted vague business questions into SQL logic.
• 🔄 Version Control – used GitHub to manage iterations of queries and track my progress.

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📈 Insights

Here’s a summary of what the data revealed:

1. Remote analyst roles can pay extremely high salaries (up to $650k).
2. SQL is the #1 skill → it’s both the most demanded and required for top-paying roles.
3. Python + Visualization tools are crucial companions for data storytelling.
4. Big Data & Cloud tools (PySpark, Databricks, AWS) are the future differentiators for higher salaries.
5. An optimal skill set combines fundamentals with advanced, modern tools.

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✅ Conclusion

This project confirmed the importance of SQL as a cornerstone skill while highlighting how additional tools (Python, Tableau, Cloud platforms) elevate both demand and salary potential.

For aspiring analysts, the roadmap is clear:

1. Master SQL and Excel – the foundation.
2. Add Python & BI tools – for versatility.
3. Learn Cloud & Big Data tools – to stay ahead of industry trends.

Ultimately, the job market rewards continuous learning and adaptability. This project was not just about answering questions with SQL — it was about building a framework for data-driven career planning.

✨ Created by Nadia Rozman | September 2025