Manager, People Analytics

Responsibilities

• This new role will report directly to the Global Head of Workplace Engineering, and sits within our Data discipline inside of our People department. Experienced data professionals at Canonical live at the intersection of software, data, and the business. They don't just help surface analytic artifacts; they help transform them into a strategic asset that elevates decision making and innovation across our global operations (e.g., sales, marketing, engineering, and human resources). They balance technical precision with business pragmatism, designing and iterating on experiments, automations, and products that transform insights into valued outcomes. They shape tomorrow's AI/Data stack in open source, ensuring that Canonical remains at the forefront of data-driven excellence. Their curiosity is contagious and has an amplifying effect on data literacy and stewardship across the business teams they support.
• Multi-Disciplinary Leadership: Lead and mentor a specialized squad of People Data Scientists, Software Engineers, and UX Designers. You aren't just managing analysts; you are leading a product squad.
• Data Stewardship & Product Storytelling: Act as the "Translator-in-Chief". Take complex data models and turn them into compelling narratives. Lead the development of AI Agents that can surface insights to managers in real-time. Ensure the "People Data Stack" is secure, GDPR/privacy-compliant, and acts as a "Single Source of Truth" across all People related offerings, products and services.
• Proven ability to lead and drive others to execute against well planned roadmaps, with a strong mentor/coaching mindset.
• Ambition to build tomorrow's AI/Data stack in open source.
• Reliable, delivering high quality work that builds trust and confidence.
• Accessible, collaborating to build products and systems that are usable.
• Experience with dbt (Data Build Tool), Airbyte, Ranger, Superset, Temporal, or Trino
• Product management
• User experience and design

Languages

Required

• Background in data science, mathematics, or engineering, skilled in advanced statistics, data wrangling/science, databases (SQL, etc.) or software engineering.
• Strength in coding/scripting languages (Python, R, JavaScript, etc.), software engineering, visualization (e.g. Superset, Tableau), and automation.

Nice to have

• Business level written and spoken English.
• Experience with dbt (Data Build Tool), Airbyte, Ranger, Superset, Temporal, or Trino
• Basic understanding of containerization technologies such as Docker and Kubernetes
• Product management

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