Data Architect - Canonical Data Modelling & EDM

ace has been engaged by a leading UK-headquartered wealth and asset management firm to support a multi-year technology transformation programme. The organisation manages assets across discretionary wealth, Responsible Investment (RI), charity, and institutional mandates, and is undergoing significant change: custody outsourcing to a global custodian, consolidation of its data platform on Snowflake, and a strategic initiative to build enterprise data foundations that will underpin AI, analytics, and regulatory capability for the next five years. 

We are seeking an experienced Data Architect to define, evolve, and govern the canonical data models that will underpin the firm's investment, client, and operational data domains. This is a high-impact role focused on models that get implemented and adopted, not documentation only. The successful candidate will bridge modelling and engineering: translating logical models into implementation-ready artefacts consumed by Snowflake, downstream analytics, and integration layers. 

This role is critical to enabling the organisation to move from fragmented, siloed data to a coherent, governed enterprise data model that powers scalable growth. This is a full-time position for an initial 3-month contract, for a Q3 start date.

Responsibilities:

Canonical Data Modelling 

• Define, evolve, and govern enterprise canonical data models across the firm's core buy-side data domains: positions, valuations, transactions, orders, client/AUM data, reference data, ESG/RI data, and benchmarks. 

• Model the investment lifecycle end-to-end: order generation, execution, allocation, settlement, corporate actions, performance attribution, and accounting impacts (IBOR/ABOR). 

• Establish clear semantics, entity relationships, identifiers, lineage, and versioning across all canonical models. 

• Define golden-source ownership for each data domain and document data lineage from source system to enterprise model to consumption layer. 

Standards & Industry Frameworks 

• Apply and map relevant buy-side industry standards including ISO 20022, FIX protocol, SWIFT messaging, GIPS, and relevant regulatory reporting frameworks. 

• Evaluate and apply Open Standards where applicable (e.g., FINOS CDM for buy-side, FinDaF data vocabulary) to accelerate canonical model development. 

• Ensure ESG and Responsible Investment data structures meet evolving regulatory and client disclosure requirements (e.g., SFDR, TCFD, SDR). 

Data Domains & Integration 

• Lead modelling for ingestion and harmonisation of: 

• Investment data: positions, trades, allocations, corporate actions, valuations, performance 

• Client data: AUM, mandate structures, client hierarchies, fee agreements, suitability profiles 

• Market & reference data: pricing, benchmarks, security master, counterparties, identifiers (ISIN, SEDOL, LEI) 

• ESG/RI data: ESG scores, engagement records, screening criteria, voting data, impact metrics 

• Operational data: reconciliations, exceptions, custody instructions, settlement status 

• Define data models to support the firm's custody outsourcing programme, including data exchange specifications with the global custodian. 

• Partner with Snowflake engineering teams to ensure canonical models are correctly implemented as Snowflake schemas, including transformation logic, data quality rules, and access patterns. 

Stakeholder & Domain Collaboration 

• Work across Investment Management, Portfolio Management, Investment Operations, Distribution, Finance, and Compliance to ensure models support valuation inputs, performance reporting, client analytics, reconciliations, and regulatory submissions. 

• Partner with the ESG/Responsible Investment team to model RI-specific data structures including company screening data, engagement history, proxy voting, and impact reporting. 

• Act as a trusted authority and translator between business domain owners and technology engineering teams. 

Implementation & Engineering Bridge 

• Translate logical models into implementation-ready artefacts: Snowflake DDL schemas, API contracts, event/topic models, validation rules, transformation specifications, and data quality rules. 

• Provide architectural input into the target-state data platform and integration patterns (e.g., event-driven ingestion, Snowflake data vault / dimensional layers, API-first integration). 

• Mentor engineers and analysts on model adoption, ensuring implementation fidelity across consuming applications. 

Governance & Standards 

• Establish model governance practices: naming conventions, business glossary, metadata standards, stewardship model, change control, documentation, and versioning. 

• Define and embed data quality rules and validation frameworks as part of the canonical model rollout. 

• Contribute to the firm's broader data governance programme, including alignment with the data ownership and stewardship model being established across the organisation.