Manager, People Analytics

Job Summary

The Manager, People Analytics leads a multi-disciplinary squad of People Data Scientists, Software Engineers, and UX Designers to manage internal workforce data assets. This manager bridges human behavioral logic with product development, builds predictive data pipelines using Python or R, automates bankable ETL pipelines, builds real-time corporate AI Agents, and safeguards the data protection parameters of the global People Data Stack.

Key Roles & Operational Responsibilities

1. Multi-Disciplinary Squad Leadership & Product Delivery

• Product Squad Ownership: Lead a dedicated, agile squad comprising People Data Scientists, Software Engineers, and UX Designers, treating workforce analytics as an internal engineering product.
• Technical Translation: Act as the primary link between business leaders and developers, translating human behavioral models and HR questions into clear research frameworks and production code.
• Roadmap Governance: Drive the engineering squad to deliver features against strict product roadmaps, combining mentorship with technical design reviews.

2. Advanced People Science, Predictive Analytics & AI Operations

• Prescriptive Engineering: Move beyond basic backward-looking metrics to engineer predictive analytics engines, analyzing "Time-Product" integration variables to forecast attrition dynamics.
• AI Agent Development: Direct the design and deployment of specialized LLM/AI Agents capable of surfacing real-time, context-aware management insights to leadership.
• Workplace Balancing: Balance high-tech automated platforms with high-touch human interventions, optimizing employee workflows to maximize human time value.

3. Open-Source Infrastructure & Data Privacy Governance

• Open Source Development: Contribute directly to the architecture of open-source data infrastructures, utilizing platforms to advance internal data pipelines.
• GDPR & Privacy Enforcement: Serve as the primary guardian of the corporate People Data Stack, securing complete GDPR/privacy compliance while maintaining a centralized "Single Source of Truth."
• Data Stewardship: Champion data literacy campaigns across corporate units (Sales, Engineering, Marketing, HR) to ensure reliable analytics stewardship.

Foundational Capabilities & Prerequisites

Required Technical Profile & Experience

• Academic Foundation: Exceptional academic track record across both high school and university levels within Data Science, Mathematics, Engineering, or a highly quantitative science discipline.
• Technical Engineering Edge: Proven, practical capabilities in advanced statistical analysis, data wrangling methodologies, and relational database management (SQL).
• Coding Command: Deep hands-on experience writing clean scripts in languages such as Python, R, or JavaScript.
• Visualization Mastery: Experience configuring interactive dashboard systems using platforms like Apache Superset, Tableau, or equivalent visualization tools.
• Methodological Rigor: Demonstrated capacity to turn open-ended business questions into structured research methodologies, synthesizing data to guide business outcomes.
• Language Proficiency: Business-level fluency in written and spoken English.
• Personal Alignment: Direct, sincere personal motivation aligned with Canonical's open-source philosophy and high-performance engineering culture.

Nice-to-Have Skills

• Practical experience working with data orchestration tools: Airbyte, Ranger, Superset, Temporal, or Trino.
• Basic working knowledge of containerization setups using Docker and Kubernetes.
• Functional experience in Agentic Engineering frameworks or LLM system prompt setups.
• Proven experience in formal Product Management or User Experience (UX) Design.

What Canonical Offers

• Bi-Annual Review Model: Performance assessments and compensation adjustments executed twice per year to reward outstanding technical contributions.
• Generous Annual Leave: 40 days of annual leave per year, encompassing regional public holidays and dedicated all-company holiday periods.
• Learning Budget: A personal continuous development allowance of USD 2,000 per year to back technical learning objectives.
• Distributed Compensation Strategy: Competitive salary structures balanced across local geographical frameworks, coupled with annual performance-driven bonus opportunities.
• Comprehensive Care Infrastructure: Comprehensive family support benefits, including structured maternity/paternity leave and a holistic Team Member Assistance Program.

Skills Required:

• Computer / Software / It / Data