Enterprise AI is forcing organisations to rethink their data estates. Data platforms designed mainly for reporting are often not enough for GenAI, semantic search, agentic workflows and AI-enabled decision-making. Clients now need data that is trusted, governed, contextualised and consumable by both people and intelligent systems.
We are looking for client-facing Enterprise Data Architects to join our growing Enterprise AI practice. You will help clients transform fragmented data estates into AI-ready foundations, advising on architecture decisions across cloud data platforms, lakehouse and warehouse patterns, data products, semantic layers, metadata, lineage, governance, knowledge graphs and GenAI retrieval patterns.
This is a consulting role, not a purely internal architecture role. You will diagnose ambiguous client problems, shape options, make trade-offs explicit, and translate complex data architecture issues into clear decisions for both technical teams and executive stakeholders.
You will work in cross-functional teams alongside product owners, data scientists, ML and GenAI engineers, data engineers, business analysts and client stakeholders.
Typical outputs may include target-state architectures, maturity assessments, platform option appraisals, data product designs, governance models, lineage maps, ontology and semantic models, integration patterns, GenAI data-readiness assessments and implementation roadmaps.
We are hiring across several levels. At earlier levels, we expect strong architecture delivery experience and hands-on platform understanding. At senior levels, we expect the ability to shape enterprise data strategy, influence senior stakeholders, lead complex architecture decisions and guide multi-disciplinary delivery teams.
We do not expect every candidate to be a specialist in every aspect of AI-ready data architecture. We are looking for architects with strong core data architecture experience and credible depth in some of the areas that matter for AI-enabled data estates, such as governance, semantic modelling, lakehouse architecture, data products, metadata management, knowledge graphs, RAG or enterprise data strategy.