N Family Club

• Embed with support office departments (Finance, People, Admissions, Marketing, and others) to map current processes in detail • Identify which processes are candidates for automation — where is the volume, where is the pain, where is the manual effort that doesn't need to be manual? • Write clear requirements and specifications that our Automation Engineer can build from • Prioritise opportunities by business impact • Work closely with the Automation Engineer during build and testing to ensure what gets built actually solves the problem • Own the rollout when automations and new analytics tools go live in a department • Train users, manage the transition from old process to new, handle resistance constructively • Collect feedback and iterate — if something isn't working, you're the first to know and the first to act • Build lasting relationships with department heads so they see the Data & AI Team as a partner, not a threat • Run AI fluency cohorts across the organisation — practical sessions helping teams use tools like Claude, Gemini, and others to improve their personal productivity • Curate which AI tools are appropriate for which teams and use cases • Identify who's struggling and provide targeted support • Feed back to the Director what the organisation is trying to do with AI, so we can prioritise tooling and training • While embedded with departments, surface data and analytics opportunities — teams making decisions on gut feel, questions that could be answered with data, reports that could be dashboards • Feed these into the Product Owner's backlog for the Analytics Engineer to deliver

• Take ownership of and expand our transformation layer using DBT, ensuring new models align with the established architectural patterns. • Design and extend dimensional models using frameworks like Kimball or BEAM, ensuring the schema accurately reflects the nuance of our business operations. • Work with BigQuery. • Establish data quality monitoring and testing frameworks. • Act as a bridge between business and data: Deeply analyse business workflows to gather requirements and translate them into reliable facts and dimensions. • Document data definitions and business logic, ensuring the "why" behind the modelling decisions is clear. • Maintain the long-term health and optimisation of the transformation layer.