Senior Information Architect-London

The role will provide design authority and governance across conceptual logical and physical data models operating across the firms cloud data platforms and contributing to AIdriven data products.

Data Modelling & Architecture

• Lead and contribute with conceptual logical and physical data model design aligned to the firms data mesh and medallion architectures

• Govern and maintain the enterprise data model library in SqlDBM enforcing naming conventions domain standards and versioning

• Define and steward canonical data entities and lineage across source integration and consumption layers

• Help with the translation of business requirements into rigorous data structures in collaboration with engineering and domain teams

• Guide teams on the design and effective use of semantic data models including their application to agentic AI solutions
  

Semantic & Knowledge Modelling

• Integrate semantic layers with business glossaries data catalogues and lineage tooling

• Design and maintain semantic models to support agentic-AI capabilities

• Help develop ontologies taxonomies and controlled vocabularies underpinning enterprise metadata and search

• Define semantic modelling standards and patterns to ensure consistent meaning interoperability and AI readiness across data products

• Guide and control the creation of data domains that align with the banks underlying capabilities
  

Data Engineering & Platforms

• Design data product schemas across Snowflake and Databricks including Delta Lake structures and access patterns

• Define and enforce architectural guardrails for data product design including performance scalability security and cost efficiency

• Set design standards and review transformation/pipelines for compliance

• Embed data contract standards and data quality frameworks across pipeline outputs
  

Agentic AI & AI-Assisted Development

• Leverage Snowflake Cortex / Databricks Genie (or similar) to help engineering teams deliver natural language query and LLM-augmented data products

• Utilize GitHub Copilot (or similar) to accelerate modelling documentation and pipeline development

• Design and define a set of agentic skills that can be provided to implementation teams

• Contribute to the firms AI Champion network evaluating emerging capabilities and advising on adoption

Enterprise Architecture & Design Assurance

• Act as a design authority for data architecture across teams and data domains

• Partner with enterprise security and risk architecture to ensure data solutions align with broader technology and regulatory standards

• Provide early-stage architectural input to initiative discovery and solution design to reduce delivery risk
  

Governance & Stakeholder Engagement

• Present architecture decisions to senior stakeholders architecture review boards and regulatory audiences

• Support regulatory compliance through robust lineage metadata management and data quality evidence

• Mentor junior architects and engineers within the Information Architecture community of practice
  

The ideal candidate should have a fair amount of the following skills and experience:

• Proven experience in data architecture data engineering or information management

• Bachelors degree in Computer Science Information Systems Data Science Engineering or a related discipline

• Proven enterprise data modelling experience at conceptual logical and physical levels

• Hands-on SqlDBM experience (or equivalent modern toolset)

• Proven experience with dbt Labs

• Good Python skills API integration automation

• Practical Snowflake and Databricks experience including Cortex AI and Genie

• Working knowledge of OWL RDF SPARQL and enterprise ontology design

• Experience with at least one agentic AI framework

• GitHub Copilot or equivalent AI coding assistant experience in a professional setting

• Financial services background preferred; BCBS 239 awareness advantageous
  

Desirable skills include:

• Data Vault 2.1 alongside Kimball/Star-schema modelling

• Data mesh data product ownership and federated governance

• Knowledge graph platforms (Neo4j Neptune) or knowledge of OWL RDF and exposure to and build of ontologies

• Published thought leadership or community contributions in data or AI