Introduction

Diving into data job market, focusing on data analyst roles whether it be remotely or specifically localized in Europe, we explored top-paying, demanded skills and so on.

For SQL Queries, check the repo: project_sql folder

Tools I used

• SQL: The main skill required for these Analysis.
• PostgreSQL: My chosen database management tool, ideal for this job postings data.
• Visual Studio Code: My favorite IDE to manage my SQL queries and files.
• Git & GitHub: Needed to share my SQL scripts and analysis.

The Analysis

Each one of thesequeries aimed at analysing specific aspects of the data analyst job market. Here's my approach:

Query 1&3. Top Paying Data Analyst Jobs(remote/europe)

These SQL queries identify the top 10 highest paying remote Data Analyst roles. The "Anywhere" version captures global salary peaks $650k, whereas an EU version reflects regional market rates and labor laws. This comparison highlights that while global searches find extreme outliers, the EU version provides more realistic, localized benchmarks.

-- EU version
SELECT
    job_id,
    job_title,
    job_location,
    job_schedule_type,
    salary_year_avg,
    job_posted_date::Date,
    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 LIKE ANY (SELECT CONCAT(name, '%') FROM eu_countries) AND 
    salary_year_avg IS NOT NULL 
ORDER by salary_year_avg DESC
LIMIT 5;

Query 2&4. Required Skills For Top Jobs(remote/europe)

Global high-paying roles prioritize Python, R, and Tableau for analytical modeling, while the European market favors Azure, Oracle, and SQL Server, reflecting a focus on enterprise cloud infrastructure. Despite these regional differences, SQL remains the universal requirement in all positions.

-- Remote version
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 
    tpj.*,
    skills
FROM top_paying_jobs as tpj
JOIN skills_job_dim as sjd ON tpj.job_id = sjd.job_id
JOIN skills_dim as sd ON sjd.skill_id = sd.skill_id
ORDER BY
    salary_year_avg DESC;

5&6. Most Demanded skills In Data Analysis

SQL, Excel, and Python remain the undisputed market leaders globally, the primary difference appears in visualization, where the global market favors Tableau while Europe shows a clear preference for the Microsoft Power BI ecosystem.

-- Global version
WITH remotes AS (
SELECT
    skill_id,
    count(*) AS skill_count
FROM job_postings_fact as job_postings JOIN skills_job_dim as skull on job_postings.job_id=skull.job_id 
WHERE job_postings.job_title_short = 'Data Analyst'
GROUP BY skill_id
)

SELECT 
  skills as skill_name,
   skill_count
FROM remotes join skills_dim as skill on remotes.skill_id=skill.skill_id

ORDER BY skill_count DESC
LIMIT 5;

Global	Europe
1. SQL (92,628 jobs)	1. SQL (4,501 jobs)
2. Excel (67,031 jobs)	2. Excel (3,171 jobs)
3. Python (57,326 jobs)	3. Python (2,897 jobs)
4. Tableau (46,554 jobs)	4. Power BI (2,428 jobs)
5. Power BI (39,468 jobs)	5. Tableau (1,911 jobs)

7. Highest Paying Data Analyst Skills in Europe

The following query reveals the highest average salaries per skill for Data Analysts within the EU.

-- Europe version 
SELECT
    skills,
    ROUND(AVG(salary_year_avg),0) AS avg_year_sal
FROM job_postings_fact as tpj
    JOIN skills_job_dim as sjd ON tpj.job_id = sjd.job_id
    JOIN skills_dim as sd ON sjd.skill_id = sd.skill_id
WHERE 
job_title_short = 'Data Analyst' AND
salary_year_avg IS NOT NULL AND 
job_location LIKE ANY (SELECT CONCAT(name, '%') FROM eu_countries)
GROUP BY skills
ORDER BY avg_year_sal DESC
LIMIT 10;

Skill	Average Annual Salary
SVN	$400,000
Kafka	$400,000
Oracle	$282,500
Linux	$226,507
Git	$189,150
Shell	$156,500
AWS	$155,000
Smartsheet	$155,000
SQL Server	$149,397
Flow	$125,880

What I learned

This project allowed me to bridge the gap between my university theory and the real world data. I significantly sharpened my SQL proficiency by messing with this dataset.

• Advanced Querying: Improve my use of CTEs, joins, and subqueries to filter and aggregate global job market data.

• Actual Analysis: Developed the ability to identify high-value skill clusters and regional differences, transforming raw numbers into strategic career insights.