Senior Analyst – People Analytics & Insights

2. Data Analysis Modelling and Insight Generation
Analyse complex workforce datasets across domains such as headcount attrition retention engagement
performance talent acquisition learning DE&I workforce planning productivity and organizational
design.
Identify drivers and correlations behind key workforce outcomes such as turnover risk hiring
effectiveness engagement movement talent pipeline health and workforce productivity.
Conduct deep-dive analyses to support strategic HR initiatives HR CoE priorities and business workforce
decisions.
Translate analytical outputs into clear actionable insights and recommendations for People Analytics
Partners HR CoEs HRBPs and business leaders.
Develop insight narratives and data storytelling outputs that clearly explain findings implications and
recommended actions.
3. Dashboard Reporting and Visualization Development
Develop and maintain interactive dashboards reports and analytics products using tools such as Tableau
Power BI Qlik or similar visualization platforms.
Create intuitive and user-friendly visualizations that translate complex workforce data into clear insights.
Enhance existing reporting solutions by improving usability performance automation standardization and
adoption.
Ensure dashboards and reports are aligned with standardized HR metrics reporting governance
principles and business definitions.
Partner with People Analytics Partners to ensure analytics products meet HR CoE and business needs.

4. Data Engineering Collaboration and Analytics Enablement
Work with HR Technology Data Engineering and enterprise data teams to access structure validate and
transform HR data for analytics use.
Collaborate on data pipelines data models semantic layers and curated datasets required for scalable
People Analytics solutions.
Use platforms such as Databricks Snowflake or similar enterprise data environments to support advanced
analytics and reporting.
Write and optimize SQL queries and analytical scripts to extract transform join validate and prepare
workforce data.
Support automation and repeatable data assets that reduce manual reporting effort and improve analytics
efficiency.
5. Predictive Analytics and Data Science
Build predictive and statistical models to support workforce planning attrition risk identification talent
movement analysis hiring forecasts engagement analysis and other HR priorities.
Apply techniques such as regression classification clustering forecasting segmentation driver analysis
and text analytics where relevant.
Evaluate model performance interpret results and communicate implications in a business-friendly
manner.
Ensure data science outputs are practical explainable ethical and actionable for HR stakeholders.
Contribute to experimentation and continuous improvement of advanced analytics methods across People
Analytics.

6. Data Quality Governance and Metric Standardization
Validate HR datasets metrics definitions calculations and reporting logic to ensure accuracy and
consistency.
Identify and investigate data quality issues impacting dashboards models reports or insights.
Partner with HR Systems Data Governance and People Analytics teams to resolve data issues and
improve data readiness.
Document data sources assumptions transformation logic business rules and metric definitions.
Ensure analytics solutions comply with data privacy access control and responsible analytics principles.
7. Stakeholder Partnership and Delivery
Partner with People Analytics Partners to understand priorities clarify requirements and deliver analytics
solutions aligned to HR CoE roadmaps.
Collaborate with stakeholders across Talent Acquisition Talent Management Total Rewards DE&I
Employee Relations Workforce Planning HR Operations and HRBPs.
Manage multiple analytics deliverables balancing speed quality stakeholder needs and scalability.
Present analytical findings solution prototypes and recommendations to People Analytics stakeholders.
Contribute to a culture of data-driven decision-making across HR.
Required Qualifications:
Bachelors degree in Data Science Statistics Mathematics Computer Science Engineering Economics
Business Analytics HR Analytics or a related field.
5 years of experience in people analytics workforce analytics HR analytics business analytics data

analytics or data science.
Strong hands-on experience developing dashboards and analytics solutions using Tableau Power BI Qlik
or similar BI/visualization tools.
Experience working with enterprise data platforms such as Databricks Snowflake cloud data
warehouses lakehouses or similar environments.
Strong proficiency in SQL for data extraction transformation joining validation and analysis.
Experience using Python R or similar analytical programming languages for data analysis automation
statistical analysis or modelling.
Experience working with HR datasets such as headcount attrition engagement hiring talent
performance compensation DE&I learning or workforce planning.
Demonstrated ability to translate complex data into clear insights recommendations and business-ready
narratives.
Strong understanding of data quality metric definitions governance and reporting logic.
Ability to work with cross-functional stakeholders across HR technology data and business teams.
Preferred Qualifications:
Experience in predictive analytics machine learning statistical modelling forecasting workforce
segmentation or driver analysis.
Experience with Databricks for data preparation analytics development notebook-based analysis or
machine learning workflows.
Experience with HR platforms such as SuccessFactors Workday or similar HRIS systems.
Experience with SuccessFactors Story Reporting People Analytics or similar HR reporting capabilities.
Experience in global organizations shared services matrixed environments or regulated industries.
Understanding of HR processes across Talent Acquisition Talent Management Total Rewards DE&I
Workforce Planning and Employee Experience.
Experience developing reusable analytics products curated datasets semantic layers or automated
reporting pipelines.
Familiarity with responsible AI ethical analytics privacy and people data governance.