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Why This Dataset Was Chosen
Why This Dataset Was Chosen
This data set sparked my interest having pivoted in my career to IT. This data allowed me to develop an overview of the IT roles, experience, and salaries this field. I was able to answer the following questions in regard to salary via this assessment:
Which salary pays the most by country?
What type of work (FT, PT, Freelance or Contract) follow a trend in salary by experience level?
Which job role titles should I consider for a path in IT based on location, experience and salary?
Do those with fully remote positions have a salary approximate to those that are 'not' remote?
What is the average salary trend over time?
Which countries pay the most in USD behind the US?
Do those with more experience have a higher remote ratio?
Source Code and Files Used
This project has been my most personally meaningful endeavor to date, requiring numerous hours of work over the course of several months. While the analytical portion was relatively straightforward—requiring minimal data cleaning and no complex joins—a substantial amount of time was devoted to developing Tableau visualizations, which proved to be both engaging and technically demanding. I am particularly proud of the final dashboard, which reflects my commitment to continuous learning and perseverance throughout the project.
This project has been my most personally meaningful endeavor to date, requiring numerous hours of work over the course of several months. While the analytical portion was relatively straightforward—requiring minimal data cleaning and no complex joins—a substantial amount of time was devoted to developing Tableau visualizations, which proved to be both engaging and technically demanding. I am particularly proud of the final dashboard, which reflects my commitment to continuous learning and perseverance throughout the project.
The steps I took..
The steps I took..
I found this dataset via Kaggle and read the file into a Databricks notebook specifically using SQL. After the metadata of the file was reviewed, and determining the quality, descriptive analysis was conducted. The medallion architecture data management framework within the databricks lakehouse to improve data quality for refinement. Finally, the data was exported to Tableau for more extensive visualizations than what Databricks could provide.
Why I took these steps
Why I took these steps
First, I wanted to identify the scale of the data as well as its metadata, this in turn will allow me to know how the data must be handled depending on its attributes. Next, a few visualizations were created using Databricks Dashboards to reflect a few SQL queries for work type, remote ratio, and salary by work year.
Tools I used...
Tools I used...
🖥️Databricks
📊Tableau
💡SQL
Challenges: Databricks SQL analysis seemed to require more steps to attain data for the visualizations thereby having less abilities than use of PySpark for analysis.
Challenges: Databricks SQL analysis seemed to require more steps to attain data for the visualizations thereby having less abilities than use of PySpark for analysis.
Tableau Visualization Examples - See Dashboard Link
Tableau Visualization Examples - See Dashboard Link
Databricks SQL Visualizations
Databricks SQL Visualizations
Results
Results
My analysis of the dataset revealed several strong compensation patterns across roles, experience levels, employment types, and geography. Machine Learning (ML) and Artificial Intelligence (AI) roles consistently showed salaries that were highly competitive, often matching (and in some cases rivaling) compensation levels typically associated with Engineer, Manager, and Executive positions.
When comparing employment arrangements, experienced professionals working in Full-Time and Contract roles earned noticeably higher salaries than those providing Freelance services, indicating stronger earning potential in more structured employment types.
Experience level also played a major role in compensation. Senior-level professionals had the highest salary ratio overall, followed closely by Mid-level roles. Entry-level and Executive-level roles showed similar ratios, although the salary gap between those two levels was the largest, highlighting the sharp difference in pay at the top end of leadership roles.
From a geographic standpoint, the United States ranked highest in salary by role, with Canada emerging as the second-highest-paying location, showing strong earning potential outside the U.S. market as well.
Lastly, remote work trends showed that remote roles represented only about 38% compared to on-site positions, suggesting that the majority of opportunities in this dataset still lean toward on-site work.
Recommendations
Recommendations
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