THE ROLEPartner directly with business and product teams to identify, scope, and build agentic workflows, AI agents, and reusable skills that solve real problemsAdvise on the platform decisions for where automations and agents are hosted (e.g. n8n, Databricks Apps, Claude-based tooling), balancing speed of delivery against maintainability, cost, and governance requirementsDesign how agents and automations access data, working out the right layer to query from (governed semantic layer vs raw source), what permissions and guardrails should sit around that access, and how to avoid duplicating data prep or creating shadow pipelinesShape how data is organised to make it usable by automations in the first place — partnering with Data Engineering on cataloguing, metadata, and lineage (e.g. via OpenMetadata) so agents are working from documented, trustworthy sourcesEstablish and maintain a model for ownership and stewardship: clear stewards for data domains and business processes, and clear owners for the automations/agents built on top of them, so accountability doesn't disappear once something goes liveRun the governance scaffolding for AI/automation adoption — tiered autonomy classification, risk assessment, and lifecycle management (build → test → deploy → monitor → retire) — so the organisation can scale agentic tooling without losing control of itWork closely with the Data Architecture & Strategy Lead to prioritise which processes get automated first against the team's active strategic initiatives
