This project showcases a full HR analytics pipeline built using Google BigQuery and Looker Studio. It includes data cleaning, SQL-driven analysis, performance and satisfaction evaluation, and dashboard-based insights designed to support strategic workforce planning and retention decisions.
Organizations rely on strong workforce analytics to understand performance drivers, improve employee satisfaction, and reduce attrition.
This project analyzes employee demographics, compensation, performance metrics, satisfaction scores, and manager relationships to uncover meaningful insights for HR and leadership teams.
- Clean and prepare raw HR datasets in Google BigQuery
- Perform demographic, compensation, and performance analysis using SQL
- Build a multi-page interactive dashboard in Looker Studio
- Identify key predictors of satisfaction and attrition
- Support HR strategy with actionable, data-driven insights
- Removed duplicate EmployeeID records
- Handled null values across demographics, performance, and satisfaction fields
- Standardized categorical values (departments, roles, education levels)
- Validated numerical fields (age, salary, work-life balance, ratings)
- Ensured consistency across all HR datasets
Conducted entirely in Google BigQuery, covering:
- Demographic distributions
- Department and job-role segmentation
- Salary segmentation across departments
- Manager vs. Self performance rating comparisons
- Training participation trends vs. tenure
- Job satisfaction analysis
- Attrition patterns by demographics and job roles
Built a fully interactive Looker Studio dashboard featuring:
- Multi-page navigation
- Dynamic filters (department, role, attrition status)
- Clear, insight-focused visual design
- Visual breakdowns of demographics, performance, satisfaction, and attrition
Looker Studio Dashboard:
https://lookerstudio.google.com/u/0/reporting/69009158-f304-4b9e-9289-91641f36c3cc/page/pyFCE/edit
- Workforce demographics (age, gender, education)
- Department & job role composition
- Salary distribution and segmentation
- Manager vs. self performance ratings
- Job satisfaction patterns
- Attrition breakdown across demographics and roles
Includes:
- Age distribution
- Department share (Sales, Engineering, HR, etc.)
- Job role segmentation
- Salary analysis
- Education-level breakdown
- Attrition trends by age, gender, and marital status
🔗 Query:
https://console.cloud.google.com/bigquery?sq=1069696615414:40e790031cae4b8aac11e5a2c8435c38
Includes:
- Manager vs. self performance ratings
- Job satisfaction patterns
- Manager relationship satisfaction
- Tenure vs. training participation
- Performance differences across departments
🔗 Query:
https://console.cloud.google.com/bigquery?sq=1069696615414:a6e539f9da734be99fff329710eff72e
Includes:
- Duplicate removal
- Null handling strategy design
- Standardizing job role & department names
- Normalizing numeric ranges (salary, age, ratings)
🔗 Query:
https://console.cloud.google.com/bigquery?sq=1069696615414:8c6c4aa2ee224bc9b4d49e0a8bbc1d54
- Core workforce falls between ages 25–38
- Sales represents ~65% of employees
- Most employees hold Bachelor’s or Master’s degrees
- Typical salary range: 50K–150K
- Engineering & Data Science roles score well in satisfaction
- HR is the smallest department
- Work-life balance varies widely by team
- Manager relationship is a major driver of satisfaction
- Ratings are balanced across Meets, Exceeds, and Above & Beyond
- Training participation increases with tenure and correlates with satisfaction
Higher attrition found among:
- Younger employees (18–31)
- High-workload roles
- Employees reporting low satisfaction
- Teams with inconsistent manager–employee relationships
These insights support targeted retention strategies such as improved onboarding, leadership training, and workload adjustments.
- Google BigQuery
- SQL
- Google Looker Studio
- Google Cloud Platform
Demonstrates ability to:
- Build an end-to-end analytics workflow
- Clean and transform HR datasets in BigQuery
- Perform complex SQL-based analysis
- Design interactive Looker dashboards
- Translate analytics into strategic HR insights
- Apply storytelling techniques to workforce analytics
- Machine-learning attrition prediction model
- Statistical validation (correlation, regression)
- Python-based EDA & modeling
- Real-time dashboards via HRIS API integration