Analytics Engineer

• Data Modelling & Product Development (50%): Design, build and maintain scalable, reusable, well-documented data models for analytics, BI, product and data science use cases. Transform complex raw and intermediate data into curated datasets aligned to business needs, and develop reusable semantic layers, metrics and core entities, working with Data Engineering to ensure source structures support high-quality outputs.
• Data Quality, Testing & Documentation (25%): Build automated checks for freshness, completeness, consistency and accuracy, and establish testing standards including schema, business rule and metric validation. Maintain clear documentation so users understand datasets, definitions and intended use, improving discoverability and usability across Global.
• Business Partnership & Metric Definition (25%): Partner with Analytics, Product, Data Science and commercial stakeholders to translate requirements into robust data models. Align stakeholders on common definitions, KPIs and business logic across audience, campaign and measurement use cases, and identify where analytics engineering can improve insight, consistency and speed to value., * Think Big: Work with some of the largest and most diverse datasets in UK media, helping to unlock their value across Global.
• Own It: Build deep expertise in one or more data domains and take end-to-end ownership of key data products.
• Keep it Simple: Focus on reusable datasets and models that simplify complex data and support multiple use cases.
• Better Together: Be part of a kind, supportive team that looks out for each other and invests in a strong, inclusive culture.

What Success Looks Like

In your first few months, you’ll have:

• Learned how the team operates and uses technologies such as Snowflake, dbt and Airflow.
• Built a clear understanding of the strategic direction of Data and Analytics at Global and how it supports wider business goals.
• Integrated into Agile ceremonies such as daily stand-ups, retrospectives and backlog refinements.
• Started to build a strong understanding of Global’s datasets and how they are used across the business.

• Analytics engineering skills: Experience in an Analytics Engineering or closely related data role.
• Data modelling: Proven ability to design and maintain scalable, well-documented data models that enable multiple use cases.
• Curation tools & SQL: Experience with tools such as dbt and/or Python, and the ability to write complex, efficient SQL, ideally on cloud platforms (e.g. Snowflake).
• Orchestration & DataOps: Experience with orchestration (e.g. Airflow), git and CI/CD, and an appreciation of FinOps.
• Cloud: Experience with cloud services, ideally AWS.
• Agile ways of working: Understanding of Agile methodologies and tools such as Jira.
• Communication & delivery: Strong organisational skills and attention to detail, with the ability to explain complex technical concepts to non-technical stakeholders.
• Growth mindset: A demonstrated ability to learn new skills and pick up new technologies quickly.