Data Analyst Job Market Analysis (UK)

1. Introduction

This project contains an in-depth SQL-based analysis of the Data Analyst job market in the UK, with a focus on the most in-demand and highest-paying skills. The goal was to identify key trends, optimal skills to learn, and salary insights for professionals looking to break into or advance within the field.

2. Background

The demand for Data Analysts continues to grow, driven by the need for data-driven decision-making across industries. However, not all skills are valued equally in the job market. This project aimed to answer:

• Which skills are most frequently required in Data Analyst job postings?
• Which skills lead to the highest salaries?
• What are the most optimal skills to learn for career advancement?

3. Tools Used

• SQL for data querying and analysis
• CSV files to store the data
• Git & GitHub for version control

4. The Analysis

Key Queries and Files

The project is structured as follows:

project_sql

• 1_top_paying_jobs.sql → Identifies the highest-paying jobs
• 2_job_skills_required.sql → Extracts required skills from job postings
• 3_top_demanded_skills.sql → Analyzes skill frequency in job postings
• 4_top_paying_skills.sql → Determines the highest-paying skills
• 5_optimal_skills.sql → Identifies the best skills to learn based on demand and salary
• top_paying_skills.csv → Processed dataset for further analysis

sql_edit

• 2_dates.sql → Handles date-based job data processing
• 3_cases.sql → Manages case-based conditional logic in SQL

sql_load

• 1_create_database.sql → Creates the database schema
• 2_create_tables.sql → Defines tables for job postings and skills
• 3_modify_tables.sql → Adjusts table structures for optimized querying

5. Outcomes

Most In-Demand Skills for Data Analysts in the UK

Skill	Job Postings Requiring It
SQL	29
Excel	20
Python	19
Tableau	10
R	8

SQL is the most critical skill, followed by Excel and Python, reinforcing the importance of data querying, manipulation, and visualization.

Highest-Paying Skills

Skill	Average Salary (£)
C++	177,283
Pandas	177,283
PyTorch	177,283
NumPy	177,283
TensorFlow	177,283
MongoDB	165,000
AWS	131,438
SQL Server	120,379
Airflow	118,140
JavaScript	111,175

Key Takeaways

• Machine Learning & AI skills (TensorFlow, PyTorch, NumPy) command top salaries.
• Big Data & Cloud technologies (MongoDB, AWS, SQL Server) offer strong salaries.
• SQL-based database skills remain valuable across all salary ranges.
• DevOps & automation tools (Airflow, Kubernetes, GitHub) show increasing relevance.
• Traditional programming languages like JavaScript have lower pay in data-focused roles.

6. Queries used

1_top_paying_jobs.sql

/*
- Identify the top 10 highest-paying Data Analyst roles in the UK.
- Remove nulls
*/

SELECT
    job_id,
    job_title,
    job_location,
    job_schedule_type,
    salary_year_avg,
    job_posted_date,
    company_dim.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 LIKE '%Data Analyst%' AND
    job_location LIKE '%UK%' AND
    salary_year_avg IS NOT NULL
ORDER BY
    salary_year_avg DESC
LIMIT
    10;

2_job_skills_required.sql

/* What are the skills required for these roles? */


WITH job_paying_jobs AS (
    SELECT
        job_id,
        job_title,
        job_location,
        job_schedule_type,
        salary_year_avg,
        job_posted_date,
        company_dim.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 LIKE '%Data Analyst%' AND
        job_location LIKE '%UK%' AND
        salary_year_avg IS NOT NULL
    ORDER BY
        salary_year_avg DESC
    LIMIT
        10
)

SELECT
    job_paying_jobs.*,
    skills_dim.skills AS skill_required
FROM job_paying_jobs
INNER JOIN skills_job_dim ON job_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;

3_top_demanded_skills.sql

/*
- What are the most in-demand skills?
*/

SELECT 
    skills_dim.skills AS skill_name,
    COUNT(job_postings_fact.job_id) AS job_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 LIKE '%Data Analyst%' AND
    job_location LIKE '%UK%' AND
    salary_year_avg IS NOT NULL
GROUP BY
    skills_dim.skill_id, skill_name
ORDER BY
    job_count DESC
LIMIT 5;

4_top_paying_skills.sql

/*
What are the top skills based on salary?
*/

SELECT 
    skills_dim.skills AS skill_name,
    ROUND(AVG(job_postings_fact.salary_year_avg), 0) AS average_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 LIKE '%Data Analyst%'
    AND job_location LIKE '%UK%' 
    AND salary_year_avg IS NOT NULL
GROUP BY
    skills_dim.skill_id, skill_name
ORDER BY
    average_salary DESC
LIMIT 25;

5_optimal_skills.sql

/*
What are the most optimal skills to learn?
*/

-- combine both CTEs inside a single WITH statement, separating them with a comma

WITH skills_demand AS (
    SELECT
        skills_job_dim.skill_id,
        skills_dim.skills AS skill_name,
        COUNT(job_postings_fact.job_id) AS job_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 LIKE '%Data Analyst%'
        AND job_location LIKE '%UK%' 
        AND salary_year_avg IS NOT NULL
    GROUP BY
        skills_job_dim.skill_id, skill_name
),
average_salaries AS (
    SELECT
        skills_job_dim.skill_id,
        skills_dim.skills AS skill_name,
        ROUND(AVG(job_postings_fact.salary_year_avg), 0) AS average_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 LIKE '%Data Analyst%'
        AND job_location LIKE '%UK%' 
        AND salary_year_avg IS NOT NULL
    GROUP BY
        skills_job_dim.skill_id, skill_name
)

SELECT
    skills_demand.skill_id,
    skills_demand.skill_name,
    job_count,
    average_salary
FROM
    skills_demand
INNER JOIN
    average_salaries
ON
    skills_demand.skill_id = average_salaries.skill_id
WHERE
    job_count > 5
ORDER BY
    average_salary DESC,
    job_count DESC;

3_cases.sql

SELECT *
FROM skills_job_dim
LIMIT 10;

SELECT *
FROM job_postings_fact
LIMIT 10;

SELECT *
FROM skills_dim
LIMIT 10;


SELECT 
    COUNT(skills_job_dim.job_id) AS count_of_posts,
    skills_job_dim.skill_id,
    skills_dim.skills  AS skill_name
FROM skills_job_dim
INNER JOIN job_postings_fact ON job_postings_fact.job_id = skills_job_dim.job_id
INNER JOIN skills_dim ON skills_dim.skill_id = skills_job_dim.skill_id
WHERE job_postings_fact.job_location = 'Anywhere'
GROUP BY skills_job_dim.skill_id, skills_dim.skills
ORDER BY count_of_posts DESC
LIMIT 5;

SELECT job_title_short, salary_year_avg
FROM job_postings_fact
WHERE EXTRACT(MONTH FROM job_posted_date) IN (1, 2, 3)
AND salary_year_avg > 70000
AND job_title_short = 'Data Analyst'
ORDER BY salary_year_avg DESC
LIMIT 10;