Data Analyst Job Market — SQL Insights

This project explores the Data Analyst job market to identify:

• Top-paying roles
• Skills those roles require
• Skills most demanded by the market
• Skills tied to higher salaries
• The optimal skills to learn (where demand meets pay)

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Tools

SQL (PostgreSQL) · Visual Studio Code

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Query 1 — Top-Paying Data Analyst Jobs

Query goal: Select the highest-paying remote (“Anywhere”) Data Analyst roles.

SELECT
    job_id,
    job_title,
    salary_year_avg,
    name AS company_name
FROM job_postings_fact
LEFT JOIN company_dim ON job_postings_fact.company_id = company_dim.company_id
WHERE job_title_short = 'Data Analyst'
  AND job_location = 'Anywhere'
  AND salary_year_avg IS NOT NULL
ORDER BY salary_year_avg DESC
LIMIT 10;

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Query 2 — Skills Required for the Top-Paying Jobs

Query goal: Take the top 10 highest-paying jobs from Query 1 and list their required skills.

WITH top_paying_jobs AS (
    SELECT job_id, job_title, salary_year_avg, name AS company_name
    FROM job_postings_fact
    LEFT JOIN company_dim ON job_postings_fact.company_id = company_dim.company_id
    WHERE job_title_short = 'Data Analyst'
      AND job_location = 'Anywhere'
      AND salary_year_avg IS NOT NULL
    ORDER BY salary_year_avg DESC
    LIMIT 5
)
SELECT top_paying_jobs.*, skills
FROM top_paying_jobs
INNER JOIN skills_job_dim ON top_paying_jobs.job_id = skills_job_dim.job_id
INNER JOIN skills_dim ON skills_job_dim.skill_id = skills_dim.skill_id
ORDER BY salary_year_avg DESC;

Takeaway: Core analytics stack (SQL + Python + BI) is essential even at the top end.

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Query 3 — Most In-Demand Skills

Query goal: Rank skills by frequency across all Data Analyst postings.

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 Analyst'
GROUP BY skills
ORDER BY demand_count DESC
LIMIT 5;

Takeaway: The foundational toolkit (SQL/Excel/Python/BI) remains the market baseline.

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Query 4 — Skills Associated with Higher Salaries

Query goal: Compute the average salary for each skill across roles with salary data.

SELECT 
    skills,
    AVG(salary_year_avg) AS avg_salary
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 Analyst' AND salary_year_avg IS NOT NULL
GROUP BY skills
ORDER BY avg_salary DESC
LIMIT 5;

Takeaway: Certain engineering-oriented and niche tools bring notable salaries.

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Query 5 — Optimal Skills (High Demand × High Salary)

Query goal: On remote roles, identify skills that combine both demand and strong salaries.

SELECT
    skills_dim.skills,
    COUNT(skills_job_dim.job_id) AS demand_count,
    ROUND(AVG(job_postings_fact.salary_year_avg), 0) AS avg_salary
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 Analyst'
  AND salary_year_avg IS NOT NULL
  AND job_work_from_home = True
GROUP BY skills_dim.skills
HAVING COUNT(skills_job_dim.job_id) > 10
ORDER BY avg_salary DESC, demand_count DESC
LIMIT 5;

Takeaway: Pair the core stack with cloud/big-data platforms to hit the sweet spot of relevance + compensation.

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Overall Conclusions

• SQL is indispensable: highest demand and present in top-paying roles.
• Python + BI (Tableau/Power BI) form the must-have analyst toolkit.
• Cloud and Big Data (Snowflake, Azure, Hadoop) deliver salary advantages.
• Career roadmap: master the core (SQL, Python, BI) → add 1–2 premium platforms to access top-tier opportunities.

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