About the Role
We are hiring Data Engineers at all levels, seeking individuals who bring deep technical expertise and a clear drive to deliver measurable results.
In this role, you will lead end-to-end engineering projects, designing and building the scalable data infrastructure that underpins our business. From architecting data lakes and developing customer data platforms to delivering AI-powered intelligence solutions, you will play a central role in creating the analytical data products and foundational systems that enable data-driven decisions across multiple business lines.
This is a compelling opportunity to shape high-impact projects, work alongside senior leadership, and make a meaningful contribution to our core media business.
Roles & Responsibilities
Data Infrastructure & Architecture
- Design, build, and maintain scalable data pipelines and lake architectures that serve as the backbone of audience, product, and commercial decision-making across the organisation
- Architect and manage our data lake ecosystem, defining standards for data ingestion, storage, transformation, and access across structured and unstructured data sources
- Contribute to the evolution of our data stack, evaluating and implementing tools and technologies that improve performance, scalability, and developer experience
- Design and maintain a feature registry that serves as the single source of truth for cataloguing, lineage, ownership, and SLAs supports Data Scientists and Machine Learning Engineers with robust search, documentation, and discovery capabilities across a large feature portfolio
Data Products & Platforms
- Develop and own core data products including our customer data platform, audience intelligence and other AI-powered analytical tools, ensuring they are reliable, well-documented, and built for scale
- Build and maintain robust data models that support analytics, reporting, and machine learning use cases across multiple business lines including subscriptions, advertising, and editorial
Governance & Quality
- Establish and champion data quality standards, governance frameworks, and observability practices that ensure trust and reliability in our data across the organisation
AI & Advanced Analytics
- Partner with Data Scientists, Machine Learning Engineers, and Product teams to co-develop and deploy AI-powered solutions that drive audience growth and engagement
- Translate complex and ambiguous business requirements into well-scoped, production-grade data solutions that can operate reliably at scale in a fast-moving media environment
Capability & Knowledge Development
- Stay abreast of the latest developments in data engineering and AI, evaluating their practical applicability to our business context
- Contribute to a culture of technical excellence, knowledge sharing, and continuous improvement across the data organisation
Who are we looking for
Educational Qualifications
- An advanced degree(Master's or PhD) in Computer Science, Computer Engineering, Data Engineering, or a related quantitative field
- Candidates without a formal advanced degree who can demonstrate equivalent depth through a strong professional track record and portfolio of impactful work are equally encouraged to apply
Technical Experience
Data Products & Platforms
- Minimum 6 years of hands-on experience designing and building dataproducts such as customer data platforms, audience analytics platforms, orpersonalisation and recommendation systems
- Proven ability to deliver data products that are reliable,well-documented, and built for scale in a production environment
- Experience building AI-powered solutions, including integration withmachine learning models and intelligent data pipelines, will be highly regarded
- Experience in the media or internet industry is a strong advantage
Data Infrastructure& Architecture
- Hands-on experience architecting and maintaining data lake ecosystems, defining standards for data ingestion, storage, transformation, and access across structured and unstructured data sources
- Demonstrated experience building and managing low-latency large-scale data pipelines that serve analytics, reporting, and machine learning use cases
- Experience in designing, building and operating centralised feature store, with an emphasis on consistency, correctness and reusability between training and serving environments
- Strong proficiency with large-scale batch and streamlining data processing frameworks, and applying them in production environments
Analytics Engineering
- Experience with analytics engineering practices and tools, including data modelling, transformation layer design, and documentation standards
- Strong understanding of data warehousing concepts, dimensional modelling, and modern lake house architectures
Core Engineering Skills
- Deep proficiency in SQL, Apache Spark and at least one programming language such as Python or Scala. Coding tests may be required.
- Solid understanding of data governance best practices, data quality frameworks, and observability tooling
Functional Skills
- Excellent communication skills with the ability to translate complex technical concepts for both technical and non-technical stakeholders across product, editorial, and commercial teams
- Comfortable working in cross-functional, fast-moving environments where priorities may evolve
