Introduction

Dive into the data job market! Focusing on data analysis roles, this project explores top-paying jobs, in-demand skills, and where hgh demand meets high salary in data analytics.

SQL queries? Check the here: project_sql folder.

Background

This project reflects my desire to develop stronger data analysis skills, specifically in SQL. It is based on the course SQL for Data Analytics by Luke Barousse.

Data hails from Luke Barousse's SQL course. It's packed with insights on job titles, salaries, locations, and essential skills.

The questions answered through SQL queries were:

1. What are the top-paying data analyst jobs?
2. What skills are required for these top-paying jobs?
3. What skills are most in demand for data analysts?
4. Which skills are associated with higher salaries?
5. What are the most optimal skills to learn?

Tools I Used

For my deep dive into the data analyst job market, I harnessed the power of several key tools:

• SQL: The backbone of my analysis, allowing me to query the database and unearth critical insights.
• PostgreSQL: The chosen database managment system, ideal for handling the job posting data.
• Visual Studio Code: My go-to for database management and executing SQL queries.
• Git & GitHub: Essential for version control and sharing my SQL scripts and analysis, ensuring collaboration and project tracking.

The Analysis

Each query for this project aimed at investigating specific aspects of the data analyst job market. Here's how I approached each question:

1. Top Paying Data Analyst Jobs

To identify the highest-paying roles, I filtered data analyst positions by average yearly salary and location, focusing on remote jobs. This query highlights the high paying opportunities in the field.

SELECT
    job_id,
    job_title,
    job_location,
    job_schedule_type,
    salary_year_avg,
    job_posted_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 = 'Anywhere' AND
    salary_year_avg IS NOT NULL
ORDER BY
    salary_year_avg DESC
LIMIT 10;

Here's the breakdown of the top data analyst jobs in 2023:

• Wide Salary Range: Top 10 paying data analyst roles span from $184,000 to $650,000, indicating significant salary potential in the field.
• Diverse Employers: Companies like SmartAsset, Meta and AT&T are among those offering high salaries, showing a bread interest across different industries.
• Job Title Variety: There's a high diversity in job titles, from Data Analyst to Director of Analytics, reflecting varied roles and specializations within data analytics.

2. Top Paying Skills

In the following code, I filtered only for Data Analyst jobs and ordered them by yearly average salary, displaying the top 10 highest-paying positions along with the skills each job requires.

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 10
)

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

Here's the breakdown of the most demanded skills for data analysts in 2023, based on the top 10 highest-paying job posted:

• SQL is leading with a bold count of 8.
• Python follows closely with a bold count of 7.
• Tableau is also highly sought after, with a bold count of 6.
• Other skills like R, Snowflake, Pandas and Excel show varying degrees of demand.

Skill	Count	Description
SQL	8	Structured Query Language for database management
Python	7	General-purpose programming language for data
Tableau	6	Data visualization and business intelligence tool
R	4	Statistical computing and graphics
Snowflake	3	Cloud data warehousing platform
Pandas	3	Python library for data manipulation and analysis
Excel	3	Spreadsheet software for data analysis

3. Top Demanded Skills

To explore the most in-demand skills, I joined two tables to retrieve the skill names. I filtered the data to include only Data Analyst jobs that offer remote work using a WHERE clause. Finally, I grouped the results by skill and ordered them from the most to the least required.

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' AND
    job_work_from_home = True
GROUP BY
    skills
ORDER BY
    demand_count DESC
LIMIT 5;

The results of the code above display the following:

• SQL is clearly dominant, appearing in more than 7,000 job postings nearly 60% more than the next skill.
• Excel and Python are nearly tied, showing the importance of both spreadsheet-based and programmatic analysis.
• Visualization tools (Tableau and Power BI) round out the top 5, emphasizing the value of being able to communicate data insights visually.
• This ranking suggests that employers expect data analysts to be versatile capable of querying data, analyzing it with code or tools, and presenting results clearly.

Skill	Demand Count
SQL	7291
Excel	4611
Python	4330
Tableau	3745
Power BI	2609

4. Top Paying Skills

To discover the top-paying skills, I joined two tables to retrieve the skill names. Based on that, I calculated the average yearly salary for each skill. I then filtered the data to include only Data Analyst jobs with available salary information that offer remote work. Finally, I grouped the results by skill and ordered them from the highest to the lowest average salary.

SELECT
    skills,
    ROUND(AVG(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
ORDER BY
    avg_salary DESC
LIMIT 25;

Here's a breakdown of the results for top paying skills:

• Engineering-heavy and cloud-based skills (like PySpark, Databricks, Kubernetes, GCP) lead to higher salaries — data analysts with big data & cloud experience are in demand.
• Python data stack (Pandas, Numpy, Scikit-learn, Jupyter) remains a core foundation, but adding machine learning or automation boosts value.
• DevOps & software engineering practices (GitLab, Jenkins, Bitbucket, Linux) are increasingly expected — analysts who can contribute to production-level systems earn more.

Skills	Average Salary
pyspark	208172
bitbucket	189155
couchbase	160515
watson	160515
datarobot	155486
gitlab	154500
swift	153750
jupyter	152777
pandas	151821
elasticsearch	145000
golang	145000
numpy	143513
databricks	141907
linux	136508
kubernetes	132500
atlassian	131162
twilio	127000
airflow	126103
scikit-learn	125781
jenkins	125436
notion	125000
scala	124903
postgresql	123879
gcp	122500
microstrategy	121619

5. Optimal Skills

As the last analysis, to get the most optimal skills depending on demand and salary; helped by WITH clause I made a LEFT JOIN of the last two analysis above (Top demanded skills and Top paying skills), after combining the results of those analysis I ordered the results considering the most demanded skills and the skills with the highest average salaries.

WITH skills_demand AS (
    SELECT
        skills_dim.skill_id,
        skills_dim.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'
        AND salary_year_avg IS NOT NULL
        AND job_work_from_home = True
    GROUP BY
        skills_dim.skill_id
), average_salary AS (
    SELECT
        skills_job_dim.skill_id,
        ROUND(AVG(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_job_dim.skill_id
)

SELECT
    skills_demand.skill_id,
    skills_demand.skills,
    demand_count,
    avg_salary
FROM
    skills_demand
INNER JOIN average_salary ON skills_demand.skill_id = average_salary.skill_id
WHERE
    demand_count > 10
ORDER BY
    demand_count DESC,
    avg_salary DESC
LIMIT 15;

• SQL is the clear leader in demand, appearing in nearly 400 job postings.
• Python, R, Looker, and Snowflake all offer $100K+ average salaries, showing a strong return on investment for those skills.
• Snowflake and Azure stand out with very high salaries despite lower demand — ideal for specialization.
• Excel and PowerPoint are in high demand but relatively lower paid, reflecting their accessibility and general use.

What I Learned

Considering this is my first SQL project, I learned the most important clauses such as SELECT, FROM, WHERE, GROUP BY, HAVING, and others. I also learned how to work with joins, subqueries, and CTEs.

The best part is that I didn’t just learn the concepts and when to use them, I practiced applying them in different scenarios until I truly understood how they work.

Conclusions

This project explored the 2023 data analyst job market, revealing key insights:

• SQL and Python are essential skills, combining high demand and strong salaries.
• Tools like Tableau, Power BI, and Excel remain widely used, especially for data visualization and reporting.
• Specialized skills such as Snowflake, Azure, and big data tools offer higher salaries, making them valuable for career growth.
• Top-paying roles vary widely, showing opportunities across industries and job titles.

To stand out, data analysts should focus on a versatile skill set that blends technical ability, analytical thinking, and communication.