Omnis Partners
Data Architect (Metadata, Governance & Semantics)
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Data Architect – Metadata, Governance & Semantics
London - 3 days per week - OUTSIDE IR35
Omnis Partners is working with a leading organisation on the appointment of a Data Architect specialising in Metadata, Governance and Semantics.
This is a senior architecture role focused on designing enterprise metadata, governance and data discovery capabilities across a large and complex data estate. You’ll take ownership of the architecture spanning federated metadata, data catalogues, lineage, semantic layers, business glossaries, entity resolution and knowledge graphs.
We’re looking for someone who combines strong enterprise data architecture experience with deep knowledge of metadata and governance, while being comfortable working directly with senior stakeholders and engineering teams.
Experience within financial services, asset management, market data or fund reporting would be particularly relevant.
What you’ll be working on:
- Owning the design of a federated metadata architecture, connecting a central discovery layer with domain-level catalogues.
- Designing domain metadata models alongside the teams that own and understand the underlying data.
- Defining how metadata is created and maintained, including what can be harvested automatically, generated or enriched using LLMs, approved by human stewards or managed manually.
- Designing semantic models covering business terminology, departmental mappings, entity resolution and knowledge graphs.
- Establishing clear standards around metadata quality, stewardship, ownership, classification and governance.
- Acting as a design authority for engineering teams, reviewing integration designs and ensuring implementations remain aligned to the wider architecture.
- Working with senior client stakeholders through architecture reviews, working sessions and written design responses.
- Coordinating with parallel data security, entitlement and governance initiatives.
- Shaping phased implementation plans, effort estimates and data-readiness requirements.
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What we’re looking for:
- 8+ years' experience across data architecture, data platforms or related roles.
- At least 3 years' experience focused on metadata management, data governance or enterprise data cataloguing within a large and diverse data environment.
- Strong architectural understanding of enterprise data catalogues such as DataHub, OpenMetadata, Collibra or comparable platforms. The underlying architecture and concepts are more important than experience with one particular product.
- Good knowledge of metadata and lineage standards such as OpenLineage, Open Data Contract Standard or similar.
- Experience integrating metadata platforms with proprietary internal metadata, data and event models.
- Proven experience designing federated / hub-and-spoke metadata architectures.
- Strong understanding of data governance operating models, including ownership, stewardship, sensitivity classification and metadata quality.
- Experience designing semantic layers, business glossaries and concept registries.
- Understanding of entity resolution and knowledge graph modelling, including design-level knowledge of graph databases.
- Strong consulting and stakeholder-management capabilities, including facilitating workshops and defending architectural decisions.
- Ability to produce high-quality, client-ready architecture documentation.
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Useful experience:
Experience in any of the following would be beneficial:
- Financial-industry ontologies such as FIBO.
- Semantic search and embedding-based discovery across metadata.
- Asset management, market data or fund reporting.
- Highly regulated or on-premise data environments.
- Data residency, auditability and licence-scoped data entitlements.
- Designing metadata or discovery capabilities that can be consumed by AI agents and LLM-based applications.
- Pre-sales, discovery or solution architecture.
- Joining and providing architectural leadership within an existing programme of work.
You’ll operate across architecture, governance, semantics and emerging AI use cases, working with multiple data domains and senior stakeholders to establish how data can be consistently understood, governed and discovered across the organisation.
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