People Technology & Analytics Manager

About the role

We are seeking a People Technology & Analytics Manager to design and scale the technology, data, and analytics foundations of People & Culture across Snoonu Group. As the company expands into new markets, you will ensure the HR ecosystem supports growth and delivers data‑driven talent insights.

Key responsibilities

• Own the functional roadmap for Snoonu’s People technology ecosystem, guaranteeing scalability across businesses and markets.
• Lead maintenance, optimization, configuration, and evolution of core HR platforms and related technology.
• Identify and implement automation opportunities to simplify People & Culture processes and improve employee and manager experience.
• Partner with Technology teams and vendors on system integrations, architecture, upgrades, and troubleshooting.
• Establish standards for system ownership, access, data quality, governance, and documentation.
• Assess emerging People Technology solutions and recommend adoption based on business needs, scalability, integration, and ROI.
• Build the People Analytics foundation, moving from reporting to actionable talent intelligence.
• Define consistent workforce data structures, governance, and analytical methodologies.
• Collaborate with P&C leaders to translate business questions into data‑driven insights.
• Develop analytical frameworks covering workforce composition, hiring, retention, performance, cost, and succession planning.
• Connect people data with financial and operational metrics to illustrate talent‑business impact.

Required profile

• Proven experience leading HR technology and analytics initiatives in fast‑growing organizations.
• Strong understanding of HR information systems, data architecture, and governance.
• Ability to work cross‑functionally with People, Technology, Finance, and Business Strategy teams.
• Excellent analytical mindset with a focus on turning data into strategic recommendations.

Required skills

• Deep knowledge of HR platforms and system integration concepts.
• Experience with data governance, quality, and documentation practices.
• Ability to design and implement analytics frameworks and predictive workforce models.