Analytics Engineer

As an Analytics Engineer at Global, you will be part of the Data team, providing Analytics Engineering expertise across the business to build new data products and drive key decision making. You will be instrumental in building datasets and applying the relevant business logic that can be used by Analysts and collaborators. The Analytics Engineer will work closely with both the Data Engineering and Analytics team. They will need to apply business logic to the datasets being created alongside the Analytics team and ensure best practices are followed with Data Engineering. The Analytics Engineer will be expected to transform data so that it can be used by many different users such as Business Intelligence, Data Science and Analysts, ensuring they are all aligned on definitions and metrics.

Key Responsibilities

• Maintain and proactively develop data models & products: with a focus on reusable data assets that can deliver value across multiple use cases.
• Work with the Data Engineering and Analytics team: to structure datasets that can be used to build data products and other use cases.
• Monitoring, Testing and data quality: Building rigorous automated checks against datasets to ensure freshness and consistency.
• Documentation: Enabling easy understanding of models and the fields within them so that the users of the data can quickly understand which dataset they should use to answer a particular question.
• Identify Opportunities: Work closely with colleagues to identify key strategic opportunities where Analytics Engineering techniques can be used to improve decision making and project streams.
• Be a subject matter expert for Analytics Engineering: advising on best practices during scoping phase of projects with cross functional teams.

What You’ll Love About This Role

• Think Big: We’ve got some of the largest and most diverse data sets in UK media – with scale that continues to grow. You’ll play a role in harnessing the value in that data.
• Own It: You’ll be doing this by gaining expertise in one of our data domains.
• Keep it Simple: With a focus on reusability of data sets and models to support multiple use cases.
• Better Together: You’ll be working in a team with kind, supportive people that look out for you and help you to do the best work that you can. We put a lot of energy into our team culture and ensuring that everyone is fulfilled with their work.

What Success Looks Like

In your first few months, you’ll have:

• Learnt how the team operates and uses technologies such as Snowflake, dbt, Airflow.
• Built a clear understanding of the strategic direction of Data and Analytics at Global as well as how these feed into the wider business’ goals.
• Integrated within Agile team ceremonies such as daily stand ups, retrospectives and backlog refinements.
• Started to establish an understanding of Global’s datasets and their use in the business.

What You’ll Need

• Analytics Engineering Skills: previous experience in an Analytics Engineering position.
• Data Modelling: Proven ability to design and maintain scalable, well-documented data models that enable multiple use cases.
• Data Curation Tools: e.g. dbt or Python for data manipulation and transformation.
• SQL: ability to write complex SQL that runs efficiently especially on cloud data platforms (E.g. Snowflake).
• Orchestration: Proficiency with orchestration tools (E.g. Airflow).
• DataOps: Experience with git & CI/CD, and appreciation of FinOps.
• Cloud: Proficiency with cloud services (ideally AWS).
• Agile ways of working: Understanding of Agile methodologies and experience of using Jira.
• Can do attitude: Proactive, problem‑solving attitude with strong attention to detail.
• To be organised: Strong organisational skills with the ability to work both individually or part of a team and an eye for detail and quality.
• Great communication: An ability to break down and explain sophisticated technical concepts to business end users.
• A growth mindset: A proven ability to learn new skills and pick up new technologies quickly.