Data Analyst
Responsibilities
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Build and improve a single source of truth for Talent Acquisition data by connecting and structuring data from multiple inputs and systems.
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Create, audit, and enhance dashboards, scorecards, and reporting that help the team understand funnel health, performance, and key hiring trends.
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Proactively identify bottlenecks, changes in conversion, and other meaningful signals across the candidate journey, and explain their business impact in a simple way.
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Support the team with automation opportunities across Talent Acquisition workflows, including process improvements and AI-enabled ways of working.
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Conduct market and competitor intelligence research to help the business understand talent movement, company signals, and broader hiring opportunities.
Requirements
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Strong SQL proficiency and ability to translate TA business requirements into structured data models, dashboards, and reporting pipelines. You can extract, join, and transform data from ATS, HRIS, and other platforms.
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Strong analytical thinking paired with the ability to work through ambiguous stakeholder requests and turn them into actionable insights.
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Experience building or improving data models, dashboards, and recurring reporting that support decision-making.
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Ability to communicate clearly with stakeholders and present findings in simple, business-friendly language.
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Demonstrated proactiveness and curiosity: you don't just answer questions, you also spot issues and improvement opportunities independently.
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Daily hands-on AI adoption: You actively use AI tools in your work today (not a learner). You can set up Claude agents, scheduled tasks, and automation workflows. The team will rely on you as an internal AI consultant — when they ask "how do we get this done with AI?", you have a practical answer.
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Experience working across cross-functional teams and collaborating with stakeholders from different business areas.
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Background in Talent Acquisition, People Analytics, Talent Intelligence, or related domains is a plus, but not required.
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Familiarity with data visualisation tools is helpful, but expertise in a specific tool is not essential.
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Empathetic, adaptable, and motivated to learn a new domain quickly while contributing meaningful value.
