Data Architect

What you will do You will provide dedicated data architecture expertise to analyse the data landscape of an education organisation, produce its cartography and conceptual and logical modelling, and co-design a target data architecture and implementation roadmap with internal teams.

This is an architecture and framing assignment. You will produce models, argued options, documented decisions and an actionable roadmap. You will analyse the existing landscape, model the domain, instruct and argue data architecture options, and derive a prioritised trajectory. You will work in support of business analysts and enterprise architects, translating their business framing into data requirements and models, and in permanent coordination with solution architects who handle the application layer.

Your activities will include:

Framing and alignment

Facilitate workshops with business analysts and the programme team

Derive data objects from business capabilities and processes identified by business analysts and enterprise architects

Formulate data architecture principles applicable to the programme and align them with enterprise principles

AS-IS analysis

Inventory data sources: applications, reference data, databases, files, exchanges with administrations and establishments

Map current flows: producers, consumers, frequencies, exchange mechanisms, dependencies

Identify pain points: silos, redundancies, re-entry, latency, quality defects, areas without ownership

Domain modelling

Establish conceptual and then logical models of core business objects (pupils, staff, establishments, teaching structures, etc.)

Identify reference data, designate master systems and clarify data ownership rules

Produce a data dictionary and associated quality rules

Target architecture

Instruct feasible paradigms (data hub, lakehouse, federated mesh approach, virtualisation) and compare them in the context of constraints and resources

Confront these options with enterprise architects, solution architects and the data competence centre: the target is not predetermined and must result from this collective work

Establish usage rules for exchange and exposure patterns (API, event-driven, replication, virtualisation, analytical feeds): which pattern for which class of need and under what conditions

Position the target architecture in relation to shared platforms and services and the organisation's data strategy

Translate security, personal data protection, sovereignty and digital sobriety constraints into enforceable architecture rules

Ensure the chosen architecture does not close off analytical and AI use cases (quality, traceability, data accessibility), without pre-empting specific use cases

Data governance prerequisites

Define the minimum governance foundations needed to adopt the architecture: roles (data owner, data steward), bodies, decision processes

Specify the expected metadata repository and documented catalogue, and access rights management

Describe the concrete implementation of the "only once" principle for the scope

Trajectory and transfer

Break down the target into coherent work packages and define intermediate transition data architectures

Prioritise the roadmap according to business value and risk, in line with the programme calendar

Ensure knowledge transfer to internal teams so that deliverables remain usable after the assignment ends

What we are looking for Essential skills and experience

Expert-level conceptual and logical data modelling: entity-relationship / Merise, UML, ArchiMate; dimensional modelling

Expert-level mapping of data landscapes: sources, flows, applications, reference data; gap analysis

Expert-level understanding of data architecture paradigms: hub, lakehouse, data mesh, data fabric, virtualisation; ability to compare them and arbitrate in a constrained context

Expert-level knowledge of reference data and quality: MDM, master systems (SoR), data dictionary, metadata catalogue

Confirmed experience with exchange and exposure patterns: API, event-driven, replication, virtualisation, analytical feeds; knowledge of patterns and their implications, ability to set usage rules

Confirmed experience facilitating workshops and instructing architecture decisions

Confirmed knowledge of data governance: roles, lifecycle, access rights, GDPR, "only once" principle

Confirmed knowledge of architecture frameworks and canvases: TOGAF, ArchiMate, DAMA-DMBOK or equivalent

Confirmed experience with modelling and mapping tools (such as Sparx Enterprise Architect) and data cataloguing tools

Confirmed understanding of security and data sovereignty: translating constraints into architectur