Data Architect

Job Description

A key player in the digital transformation of the French-speaking Belgian public sector, Client operates in various fields such as:

• IT infrastructure management (networks, security, data centers, cloud),
• The development of custom business applications,
• Support for digital projects (functional analysis, UX/UI, project management),
• Cybersecurity and data protection,
• User support and training.
• With a constant focus on innovation, performance, and public service, Client regularly collaborates with external partners to strengthen its teams through IT consulting assignments. These collaborations are conducted within an ethical and professional framework, prioritizing quality and the tangible impact of the solutions delivered.

2. Mission

• Provide dedicated data architect expertise, over a limited period, to analyze the data landscape of WBE [Wallonia-Brussels Education is the organizing authority for official education in the Wallonia-Brussels Federation], produce the mapping and conceptual and logical modeling, and co-construct with the Client and WBE teams the target data architecture and its implementation trajectory.
• The mission is one of data architecture and scoping. It produces models, reasoned options, documented decisions and an actionable roadmap.
• The Data Architect analyzes the existing system, models the domain, examines and justifies data architecture options, and derives a prioritized roadmap. They work in support of business analysts and enterprise architects, adopting their business framework and translating it into requirements and data models, and in constant communication with solution architects, who are responsible for the application implementation.

Within the perimeter:

• Diagnosis of the WBE data landscape: sources, flows, repositories, pain points.
• Conceptual and logical modeling of the structuring business objects of the domain.
• Identification of reference data, master systems (SoR) and data ownership rules.
• Formulation of the principles and rules of data architecture applicable to the program.
• Team-based construction of target architecture options and instruction for the decision.
• Governance prerequisites necessary for the adoption of the architecture.
• Prioritized trajectory, work packages and transition architectures.

Domain modeling:

• Establish the conceptual and then logical model of the structuring business objects of WBE (students, staff, establishments, locations, teaching structures, etc. — to be confirmed with the business).
• Identify reference data, designate master systems and clarify data ownership rules.
• Produce the domain data dictionary and associated quality rules.

Construction of the target architecture:

• Instruct the possible paradigms (data hub, lakehouse, federated mesh approach, virtualization) and compare them in light of the context, constraints and resources of WBE and client.
• These options should be discussed with enterprise architects, solution architects and the Client data competence center: the target is not predetermined and must result from this collective work.
• Establish the rules for using exchange and exposure patterns (API, event-driven, replication, virtualization, analytics feeds): which pattern for which class of need and under what conditions. The choice of components and their implementation per application are the responsibility of the solution architects.
• Position the target architecture in relation to the shared foundations and services of Client and the company's data strategy.
• To translate the constraints of security, protection of personal data, sovereignty and digital sobriety into enforceable architectural rules.
• Ensure that the chosen architecture does not close off analytical and AI uses (quality, traceability, data accessibility), without preempting use cases.

Skills

• Facilitating workshops and informing an architectural decision
• Mapping a data landscape — sources, flows, applications, repositories; gap analysis
• Reference data and quality — MDM, master systems (SoR), data dictionary, metadata catalog
• Architectural frameworks and canvases — TOGAF, ArchiMate, DAMA-DMBOK or equivalents
• Data governance — roles, lifecycle, access rights, GDPR, the "only once" principle
• Conceptual and logical data modeling — entity-relationship / Merise, UML, ArchiMate; dimensional modeling
• Modeling and mapping tools (such as Sparx Enterprise Architect) and data cataloging tools
• Data architecture paradigms — hub, lakehouse, data mesh, data fabric, virtualization: the ability to compare them and choose between them within a constrained context
• Exchange and exposure patterns — APIs, event-driven processes, replication, virtualization, analytics: knowledge of patterns and their implications, ability to define rules for their use
• Data security and sovereignty — translating constraints into architectural rules