Accepting Applications Until
24 July 2026
Job Description
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.
Key Responsibilities
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.
What You’ll Love About This Role
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.
What Success Looks Like
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
What You’ll Need
- 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