Interim Data Modeller – Finance & HR (Global Data Model)Location: North London (hybrid)
Contract: Interim
Rate:£800 (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 RoleYou 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