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.

As part of our collaboration we will focus on two foundational capabilities required to enable safe and scalable AI adoption across the enterprise: Agentic Security and AI-Ready Data Foundations.

What project we have for you

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.

What you will do

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.

What you need for this

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. DataHub, OpenMetadata, Collibra or similar), including deployments outside managed cloud services — understanding of the concepts matters more than any specific product.
Hands-on experience with Python sufficient to read/review data engineering work (dbt models, pipelines) and collaborate closely with the Data Engineering track on this stream.
Strong practical experience with AI-assisted tools and agentic workflows, including the ability to identify relevant use cases and integrate AI capabilities into day-to-day engineering practices.
Solid knowledge of metadata and lineage standards (e.g. OpenLineage, 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.
Experience working in on-premises or regulated environments: data residency, auditability, licence-scoped data entitlements (elevated from “plus” — this is the client’s actual environment).
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.
English B2+/C1 mandatory (client-facing discovery engagement).

Will be a plus:

Knowledge of financial-industry ontologies (e.g. FIBO or similar) and a realistic view of their practical limitations.
Domain experience in asset management, market data or fund reporting — nice-to-have only.
Experience designing metadata and discovery layers consumed by AI agents — directly relevant given this is an “AI Agents” stream; strong plus, not a blocker.
Familiarity with semantic search over metadata based on embeddings.
Pre-sales, discovery/solutioning experience, or experience joining an engagement already in progress (this project starts at discovery/assessment/scoping).

What it’s like to work at Intellias

At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth.
We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law.
We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.

Skills

AI-assistedTools

Python

Data Architect (Metadata, Governance & Semantics)

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