Data Architect Lead

pWe are looking for a bData Architect /b bLead /b who is strategic, collaborative and solutions-oriented to join our bDigital Technology Strategy Solutions /b team, based in our bHQ in Brussels, Belgium. /b /p h3About The Role /h3 pAs a Data Architect Lead, you will define and evolve the target-state data architecture supporting analytics, advanced analytics, AI and data solutions. You will establish architecture principles, reference models, reusable design patterns, and data modeling and semantic standards that enable trusted, secure, interoperable, and AI-ready data capabilities. /p pYou will translate technology evolution into architecture roadmaps, principles, standards and reusable patterns that guide how data solutions are designed and delivered. Working across multiple stakeholders, you will ensure that data architectures remain consistent, scalable, reusable, interoperable, and aligned with standards. /p pThis is a highly collaborative architecture role where you will influence solution decisions, provide architecture guidance, and ensure alignment with enterprise data governance, security, privacy, and regulatory requirements. /p h3Who You’ll Work With /h3 pYou will work closely with Technology Owners, Data Designers, Data Engineers, and architecture teams, to drive consistent adoption of architecture standards and enable scalable reuse across stakeholders and delivery teams. /p pYou will join a growing and evolving team shaping the architecture and technology foundations supporting major analytics and data initiatives. You will act as a senior point of reference for data architecture, contribute to architecture and transformation forums, and advise and mentor architects, Data Designer Leads, and engineers on architecture practices and decision-making. /p h3What You’ll Do /h3 ul liEstablish and govern architecture principles, reference models, reusable design patterns, decision records, and architecture guardrails across data acquisition, integration, storage, transformation, serving, sharing, and consumption /li liDefine standards for conceptual, logical, physical, and semantic data models, including naming, approved business definitions, interoperability, and reuse across domains /li liContribute to architecture governance forums and review, challenge, and approve solution designs, documenting architecture decisions, risks, dependencies, exceptions, and remediation actions /li liEnsure architecture standards are pragmatic, testable, version-controlled, clearly owned, and supported by implementation guidance for delivery teams /li liEnsure coherent integration of data architecture across business domains and technology platforms, with clear boundaries, interfaces, and system-of-record responsibilities /li liDrive scalability, modularity, reusability, portability, interoperability, performance, and maintainability across the architecture landscape /li liPartner with Platform Owners and engineering leaders to align platform roadmaps, shared services, and technical capabilities with the target architecture /li liEmbed data governance, security, privacy, records management, resilience, and regulatory requirements into architecture standards and design decisions /li liIdentify architectural risks, technical debt, duplication, and control gaps, defining mitigation paths and following their resolution through the appropriate governance mechanisms /li liBuild and contribute to an active architecture community, promoting knowledge sharing and continuously improving architecture assets, governance effectiveness, and adoption /li /ul h3Interested? For this role, we’re looking for the following education, experience, and skills /h3 ul liStrong experience defining target-state architectures and roadmaps, leading design governance, evaluating trade-offs, and influencing senior technical and business stakeholders /li liExpert knowledge of enterprise data architecture, data integration, data products, conceptual/logical/physical modeling, semantic models, metadata, lineage, master/reference data, and information lifecycle principles /li liStrong understanding of the modern data and AI landscape, including cloud data platforms, lakehouse and warehouse patterns, event and API-based integration, analytics and BI consumption, data science, and data foundations for governed AI solutions /li liAbility to translate data governance, security, privacy, records management, resilience, and regulatory requirements into practical architecture controls and design guardrails /li liStrong systems thinking and decision-making skills, with the ability to analyze complex landscapes, dependencies, non-functional requirements, risks, and costs while balancing enterprise consistency with delivery pragmatism /li liStrong communication and facilitation skills, with the ability to make complex architecture understandable, build alignment across domains and platforms, and constructively challenge proposed solutions /li liAbility to con