AI & Data Engineer (GenAI)
Summary
Build and maintain Metabase dashboards, secure databases, and support an AI scheduling engine using SQL, BI tools, and ML concepts.
AI & Data Engineer (GenAI)
Responsibilities Dashboards & Reporting- Build, refine, and maintain Metabase dashboards that give real-time visibility into print orders, production status, fulfilment, shipments, and operational exceptions
- Partner with business users to understand their reporting needs, define the right metrics, and validate that data sources are accurate
- Investigate and resolve discrepancies in reports, ensuring dashboards stay timely, trustworthy, and genuinely useful for day-to-day decisions
- Conduct reviews across our databases to spot potential vulnerabilities or risks
- Document your findings clearly and put forward recommended security measures for management sign-off
- Keep an eye on user access levels, permissions, and general data integrity practices
- Support the training, testing, and refinement of our AI scheduling engine using historical data, live data feeds, business rules, and feedback from users
- Help roll out scheduling recommendations and, where authorised, autonomous scheduling actions - always working within limits and escalation paths set by management
- Contribute to improving the platform's recommendation accuracy over time
- Maintain clear records of database reviews, dashboard logic, AI model updates, test outcomes, and requirement changes to support internal audits and ISO compliance
- Flag security concerns, data quality issues, access anomalies, or operational red flags to your manager without delay
- Degree or Diploma in Computer Science, IT, Data Science, AI, Software Engineering, Cybersecurity, or a related field
- Open to entry-level applicants.
- Solid grounding in relational databases: SQL, schema design, access control, data integrity, and backup fundamentals
- Familiarity with BI/dashboarding tools such as Metabase, Power BI, Tableau, or similar
- Understanding of core machine learning concepts — model training, optimisation, data pipelines, and using historical/real-time data to drive predictions
- Comfortable working with AI-assisted development tools and modern AI-driven coding workflows