G MASS
Financial Services
DataArchitect-CanonicalDataModelling&EDM
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Architect - Canonical Data Modelling & EDM at G MASS. Skills: Canonical data modelling, Enterprise data foundations, Snowflake implementation, Data governance. Define canonical data models. Evolve canonical data models”
What You'll Achieve.
Enable enterprise data model; Power scalable growth; Implement and adopt models; Accelerate model development
Industry & Context.
Root cause analysis; Troubleshooting; Data quality analysis
What They're Looking For.
Must Have
5+ years experience, Experience with Snowflake, Experience with buy-side data domains, Experience with investment lifecycle, Experience with data governance, Experience with modelling fundamentals, Experience implementing models, Experience bridging modelling and engineering, Experience with Python or SQL
Nice to Have
Experience with MS Access, Experience with Charles River, Familiarity with ESG data structures, Experience with custody data exchange, Experience with SWIFT messaging, Experience with ISO 20022, Data architecture experience, Solution architecture experience, Experience with AI/ML data requirements, Experience with LLM use cases, Experience with streaming ecosystems, Experience with Kafka, Experience with Azure Event Hubs
What You'll Do.
Define canonical data models
Evolve canonical data models
Govern canonical data models
Model investment lifecycle
Establish model semantics
Establish entity relationships
Establish model identifiers
Establish model lineage
Establish model versioning
Define golden-source ownership
Document data lineage
Apply industry standards
Map industry standards
Evaluate open standards
Ensure ESG data structures
Lead modelling for ingestion
Harmonise investment data
Harmonise client data
Harmonise market data
Harmonise reference data
Harmonise operational data
Define data models for custody
Partner with Snowflake teams
Ensure models are implemented
Work across business units
Ensure models support reporting
Partner with ESG team
Model RI-specific data
Translate logical models
Produce implementation artefacts
Provide architectural input
Establish model governance
Define data quality rules
Embed data quality rules
How You'll Work.
Team & Collaboration
Cross-functional teams; Investment Management; Portfolio Management; Investment Operations; Distribution; Finance; Compliance; ESG/Responsible Investment team; Snowflake engineering teams; Technology engineering teams
Full Job Description
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 Fram
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