DATA PROFESSIONAL

Objectives

The aim of the project is to enhance workforce retention by address the disparity between work/life balance and salary satisfaction to prevent turnover in high-demand roles. Align pay scales with market leaders (Data Scientists/Engineers) to remain competitive. Targeted Skill Development, Focus training and recruitment efforts on dominant languages like Python to match professional preferences.

Database

Skills Applied

•      Data Cleaning & Transformation (Power Query)

•      Dynamic Title Creation

•      Custom Dashboard Layout & Design

Tools Used:

•      Power BI - Visualisation, Dax calculations, and Data Report.

•      Power Query – Data Cleaning and Transformation.

Dashboard Overview

The dashboard provides visual insights into the following

Key Metrics

✅Workplace Culture: Happy with Work/Life Balance.

✅Salary Satisfaction: Happy with Salary (Gauge).

✅Top Job Titles: Average Salary

✅Salary gender: Average Salary by Gender

✅Top Language: Favorite Programming Language

✅Geography Country of Origin

Insights

✅The Satisfaction Gap: Professionals are notably more satisfied with their work/life balance (5.74) than their actual compensation (4.27). This suggests that while the culture is manageable, pay may not be keeping pace with industry expectations.

✅Python Hegemony: Python is the undisputed leader in favorite programming languages, dwarfing R, C++, and Java. This indicates a highly standardized technical stack across these data roles.

✅Salary Hierarchy: There is a clear descending hierarchy in pay from Data Scientists > Data Engineers > Data Architects > Data Analysts. Students and Database Developers represent the lower end of the earning spectrum.

✅Gender Pay: The Average Salary by Gender donut chart shows a visible majority (Male) in the survey pool, highlighting a continuing gender gap in the data profession.                             

Strategy Recommendations

✅Compensation Realignment: Conduct a deep-dive audit into Salary Satisfaction. Since this is the lowest-rated metric (4.27), consider performance-based bonuses or transparent salary bands to bridge the gap.

Goal: Focus specifically on Data Analysts and "Other" categories where pay is significantly lower than Data Scientists.

 ✅Technical Stack Standardization: Invest in advanced Python training and infrastructure. Since it is the Favorite language of the workforce, supporting it leads to higher efficiency and developer happiness if needs be.

Goal: Phase out support for fringe languages that have low voter counts (like Java or C/C++) unless project specify.

✅Geographic Recruitment Focus: Leverage the high concentration of professionals in the US and India.

Goal: For US roles, focus on retention strategies (salary). for India-based roles, focus on scaling the workforce to take advantage of the large talent pool.

✅Diversity and Inclusion (D&I) Initiative Address the gender imbalance shown in the salary/participation chart.

To download the Report CLICK HERE

To download the Raw file CLICK HERE

To download Readme File CLICK HERE