Data Architect; Metadata, Governance & Semantics

Our client is a leading global investment management company headquartered in London. It manages over $228 billion in assets and serves institutional investors, pension funds, wealth managers, and other sophisticated clients worldwide. The firm specializes in quantitative investing, alternative investments, systematic trading strategies, and technology-driven asset management. Data science, machine learning, and AI are core components of its investment and research processes.

We build the data foundations that make AI useful and safe inside regulated financial firms. The value of AI is capped by the data its agents can reach: if an agent cannot find, interpret, trace or be correctly permissioned against data, the capability is useless, or worse, unsafe. Your job is to close that gap.

• 8+ years in data architecture or data platform roles, including 3+ years in metadata management, data governance or data cataloguing across a large and diverse data estate.
• Architectural experience with enterprise data catalogues (e.g. Data Hub, Open Metadata, Collibra or similar); understanding of the concepts matters more than any specific product.
• Solid knowledge of metadata and lineage standards (e.g. Open Lineage, Open Data Contract Standard or similar) and experience integrating with proprietary in-house metadata and event models.
• Proven design of federated (hub-and-spoke) metadata architectures: a central layer for identity, hierarchy and links, domain-level catalogues with rich local detail, and a clear contract between the two.
• Practical experience with data governance operating models: ownership and stewardship roles, sensitivity classification, metadata quality measurement.
• Semantic layer design: business glossaries and concept registries, resolution of term conflicts between departments, entity resolution, knowledge graph modelling; design-level knowledge of graph databases.
• Strong consulting and client-facing skills: leading working sessions, defending design decisions in written reviews, negotiating boundaries between teams with overlapping catalogue initiatives.
• Able to produce client-ready architecture documents and written review responses without editorial support.

• Knowledge of financial-industry ontologies (e.g. FIBO or similar) and a realistic view of their practical limitations.
• Familiarity with semantic search over metadata based on embeddings.
• Domain experience in asset management, market data or fund reporting.
• Experience in on-premise or regulated environments: data residency, auditability, licence-scoped data entitlements.
• Experience designing metadata and discovery layers consumed by AI agents.
• Pre-sales or discovery and solutioning experience; experience joining an engagement already in progress.

• Own the federated metadata architecture: the central discovery layer, the domain catalogues for market data and curated reporting, and the federation contract that binds them.
• Design domain metadata models in working sessions with the client teams that own the data, extending the client's existing catalogue model instead of replacing it.
• Define the approach to machine-derived metadata: what is harvested automatically, what is drafted by LLMs and approved by human stewards, what remains manual, and how its quality is scored.
• Design the semantic layer: shared business-term definitions with per-department mappings, entity resolution across sources, and the knowledge graph that supports guided discovery.
• Act as design authority for the engineering pod: review integration designs and keep parallel implementations aligned to one architecture.
• Represent the design in client governance: reviews, written responses to senior stakeholders, coordination with the client's own initiatives and with the parallel entitlement and security workstream.
• Shape phased delivery plans, effort estimates and data-readiness prerequisites for the implementation phase.

This role sits at the intersection of data engineering, AI, and financial services, solving one of the most important challenges in enterprise AI: enabling agents to securely access and reason over trusted data. You'll have the opportunity to design and build foundational platforms that combine large-scale data systems, governance, and AI technologies in highly regulated environments. It offers significant technical ownership, exposure to cutting-edge AI agent architectures, and the chance to shape how organisations safely…