Data & AI Engineer

About the role

We are looking for a Data & AI Engineer to join our Technology Department and help shape the future of reading by building secure databases, operational dashboards and AI‑driven scheduling solutions. The role combines data engineering, business intelligence and machine‑learning expertise to support end‑to‑end production workflows.

Key responsibilities

• Review existing databases, identify security risks, document findings and propose mitigation measures for manager approval.
• Design, develop, test and maintain Metabase dashboards that provide real‑time visibility of print orders, production progress, fulfilment, shipment status and operational bottlenecks.
• Collaborate with business users to gather data requirements, define metrics, validate data sources and troubleshoot reporting discrepancies.
• Assist in the development, training, testing and continuous improvement of the AI scheduling platform using historical and real‑time data, scheduling rules and user feedback.
• Support the deployment of scheduling recommendations within defined escalation and authorisation limits.
• Maintain comprehensive documentation of database reviews, dashboard logic, AI model changes and issue logs to satisfy ISO and internal audit requirements.
• Escalate security incidents, system issues, data‑quality concerns and unauthorised access promptly to the reporting manager.

Required profile

• Bachelor’s degree or diploma in Computer Science, Information Technology, Data Science, Artificial Intelligence, Software Engineering, Cybersecurity or a related field.
• Minimum 5 years of hands‑on experience with relational databases, SQL, data visualisation and AI/ML model development.
• Proven ability to work independently and communicate technical concepts to non‑technical stakeholders.

Required skills

• SQL and relational database concepts.
• Database security, access control and backup strategies.
• Dashboard development using Metabase, Power BI or Tableau.
• Machine‑learning fundamentals, AI model training and optimisation.
• Data‑pipeline design and implementation.