Data Anonymisation & Test Data Management Architect

The client is searching for an experienced Data Anonymisation & Test Data Management Architect to analyse end-to-end data flows across a complex application landscape and develop a future-proof strategy for the secure and compliant use of data in non-production environments. The architect will play a key role in defining and gaining stakeholder buy-in for an enterprise-wide approach covering data anonymisation, data pseudonymisation, synthetic data generation, Test Data Management (TDM), data selection and minimisation, protection of personal and sensitive information, preservation of referential integrity and business usability, and management of structured and unstructured data in non-production environments.

Acting as a bridge between business and technology teams, the consultant will align business requirements, regulatory obligations, technical constraints, and operational needs to deliver a practical and sustainable strategy.

Context: The client is a large Belgian organisation undergoing significant digital transformation and continuous optimisation of its business processes and supporting information systems. To support evolving business, regulatory, and technology requirements, the organisation aims to establish an integrated strategy for the secure, compliant, and efficient use of data within testing, development, and other non-production environments. The selected consultant will assess the current landscape, understand stakeholder needs, and define a target operating model and roadmap for data anonymisation and test data management across the organisation.

Key Responsibilities: Current-State Assessment: analyse end-to-end data flows across the application landscape, identify where personal and sensitive data is processed and replicated, assess existing test data management practices and challenges, review the use of both structured and unstructured data in non-production environments, and identify privacy, compliance, and security risks.

Strategy Definition: define an enterprise-wide strategy for data anonymisation, pseudonymisation, synthetic data generation, test data provisioning, data minimisation, and sensitive data protection; establish governance principles, standards, and best practices; ensure business usability of data while maintaining regulatory compliance.

Stakeholder Engagement: collaborate with business departments, Data Governance and Data Management teams, Privacy and Compliance functions, Information Security teams, Enterprise Architecture, IT Operations, Application Management teams, Testing and Quality Assurance teams, internal business stakeholders, and external vendors and service providers.

Solution & Roadmap Development: evaluate available approaches, tools, and market solutions; define a target architecture and operating model; develop a pragmatic implementation roadmap; provide recommendations for governance, processes, and supporting technologies.

Compliance & Risk Management: ensure alignment with GDPR and privacy regulations, embed privacy-by-design and security-by-design principles, support risk assessments related to non-production data usage, and balance compliance requirements with business and operational needs.

Expected Deliverables: current-state assessment and gap analysis, end-to-end data flow and sensitive data mapping, stakeholder needs assessment, target-state architecture and operating model, enterprise-wide data anonymisation and test data management strategy, governance framework and operating principles, prioritised implementation roadmap, and recommendations regarding tools, processes, and organisational responsibilities.

Required Experience: proven experience as a Data Architect, Enterprise Architect, Data Management Consultant, or similar role; strong expertise in data anonymisation and masking, pseudonymisation techniques, synthetic data generation, Test Data Management (TDM), and data privacy and protection; experience working in complex enterprise environments; strong stakeholder management and facilitation skills; and ability to translate business requirements into pragmatic architectural solutions.

Knowledge & Competencies: GDPR and data protection regulations, Data Governance frameworks, Information Security principles, Enterprise and Data Architecture practices, structured and unstructured data environments, test and development lifecycle processes, strong analytical, communication, and consensus-building skills, and a strategic mindset combined with a hands-on and pragmatic approach.