Senior Analytics Engineer

Your Role: Senior Analytics Engineer

A hands-on analytics engineering role focused on building trusted, scalable data models that power insight, measurement and AI-driven decision-making.

As a Senior Analytics Engineer at Global, you will:

• Data Modelling & Product Development (50%): Design, build and maintain scalable, reusable and well-documented data models and curated datasets that support analytics, BI, product and data science use cases. Translate complex raw data into trusted, business-aligned datasets.

• Data Quality, Testing & Documentation (25%): Implement robust testing frameworks and automated checks to ensure data accuracy, consistency and reliability. Maintain clear documentation and improve discoverability of datasets and metrics.

• Business Partnership & Metric Definition (25%): Work closely with Analytics, Product, Data Science and commercial teams to define and align on KPIs, business logic and data definitions. Ensure datasets support consistent decision-making across the organisation.

Think Big: Help build foundational analytics models and standards for a next-generation AI-driven intelligence platform.

Own It: Take responsibility for trusted datasets and business logic that underpin key commercial and product decisions.

Keep it Simple: Turn complex, messy data into clear, reusable and well-structured data products.

Better Together: Collaborate across Data Engineering, Product, Analytics and Commercial teams to solve real-world problems.

In your first few months, you’ll have:
• Built a strong understanding of the Global:IQ vision and key use cases
• Delivered curated datasets supporting key targeting, optimisation or measurement needs
• Established consistent business logic, definitions and KPIs across teams
• Improved testing, documentation and data quality practices for core models
• Embedded yourself into agile delivery processes and cross-functional teams
• Identified opportunities to improve scalability, clarity and reusability of data models

• Analytics Engineering Experience: Background in analytics engineering or a similar data modelling-focused role
• SQL Expertise: Strong SQL skills with experience using cloud data platforms (e.g. Snowflake)
• Data Modelling Skills: Proven ability to design scalable, well-structured and reusable data models
• Tooling Experience: Experience with dbt, Python, Airflow or similar modern data stack tools
• DataOps Practices: Familiarity with git, CI/CD and testing frameworks for data pipelines
• Data Quality Focus: Strong understanding of validation, documentation and testing best practices
• Stakeholder Collaboration: Ability to translate business needs into robust analytical datasets and definitions
• Communication Skills: Able to explain technical concepts clearly to both technical and non-technical audiences
• Mindset: Detail-oriented, pragmatic, proactive and comfortable working in fast-moving environments