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 dat