Interim Data Modeller Finance & HR (Global Data Model) Location: North London (hybrid)
Contract: Interim
Rate: £800 per day (outside IR35)
A global organisation based in North London is seeking a highly experienced Interim Data Modeller to design and implement a global finance and HR data model spanning multiple source systems .
This is a pure data modelling role. The focus is on structure, consistency, and correctness of data, not reporting, dashboards, or downstream analytics delivery.
For the current financial year, the scope is predominantly Finance and HR data, forming the foundation for wider enterprise data integration.
The Role You will take ownership of the logical and physical data models underpinning global Finance and HR domains, ensuring they can scale across geographies, entities, and systems.
Key responsibilities include:
Designing and owning global canonical data models for Finance and HR
Integrating data from multiple source systems (ERP, HRIS, payroll, planning tools)
Defining conformed dimensions, facts, hierarchies, and reference data
Resolving data conflicts across regions, entities, and legacy systems
Working closely with Finance and HR stakeholders to validate definitions and structures
Producing clear, robust data model documentation suitable for long-term use
Supporting data architects and engineers to ensure accurate implementation
Required Experience Extensive experience as a Data Modeller in complex, enterprise environments
Deep expertise in Finance data modelling (GL, cost centres, entities, reporting hierarchies, actuals, budgets, forecasts)
Strong exposure to HR data models (employee, organisation, payroll, time, rewards)
Proven experience working with multiple global source systems
Strong understanding of data governance, master data, and data quality principles
Excellent communication skills, able to work directly with senior Finance and HR stakeholders
Why This Role High-impact foundational work shaping how global Finance and HR data is structured
Clear remit focused on modelling quality and long-term sustainability
Opportunity to influence enterprise-wide data standards
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