Data Architect

Arηs Group, part of Accenture, is looking for a Data Architect to support the strategic transformation of a client’s data and analytics landscape within the EU institutions in Brussels.

Context

The objective is to assess and modernize existing Business Intelligence (BI), Data Warehouse (DWH), data integration, and reporting environments while preparing a roadmap towards future data platforms and AI-ready data architectures. The environment is international, multicultural, and highly collaborative, requiring strong technical expertise, professionalism, and attention to data governance and quality.

The Data Architect will play a key role in evaluating current data ecosystems, defining target architectures, supporting migration initiatives, and improving data quality, metadata management, and integration capabilities across the organization.

Role & responsibilities

• Leading the technical assessment of corporate sovereign data, ETL/integration and reporting/dashboarding platforms proposed against the client’s business requirements, data landscape, technical specificities, performance, scalability, security and integration needs. • Translating business and migration requirements into technical specifications and target data architectures supporting immediate operational needs and longer-term evolution. • Analyzing the existing BI/DWH architecture, databases, data models, ETL processes, integrations, reports and dashboards to identify dependencies, gaps, migration constraints and technical risks. • Designing the target architecture and migration approach, including transition scenarios, data mappings, dependencies, sequencing, validation, cutover and, where relevant, coexistence of existing and target solutions. • Preparing and supporting technical proofs of concept to validate proposed solutions against representative client use cases and data. • Preparing and monitoring the technical implementation of migrations, working closely with the BI/DWH team and corporate service providers to ensure data integrity, service continuity and successful transition. • Designing data integration solutions using ETL/ELT processes and tools, and developing or adapting jobs and pipelines required for migration and operational needs. • Creating, adapting and optimizing conceptual, logical and physical data models supporting existing and target environments. • Performing hands-on technical work when required, including database-level analysis, SQL, data profiling and mapping, ETL/ELT development, troubleshooting, validation, optimization and migration, monitoring and optimizing data systems, databases and pipelines for performance, scalability and reliability. • Maintaining and improving technical and business metadata, data lineage and documentation to support accuracy, traceability, integration and reuse. • Implementing data profiling, validation and cleansing processes to ensure data accuracy, consistency and reliability. • Contributing to data governance practices, including data quality, lineage, access controls, metadata and data cataloguing, in alignment with client standards and services. • Working closely with business users, data engineers, BI and AI specialists and other technical teams to ensure that data architectures meet operational and emerging needs. • Contributing to making the client’s data increasingly suitable for AI and agentic use through appropriate data structures, identifiers, relationships, metadata, provenance and other machine-usable representations, while ensuring compatibility with established BI and data-management needs. • Assessing and, where relevant, prototyping modern data-management technologies and approaches, including SQL/NoSQL databases and semantic or graph-based representations, where they provide concrete value for the client’s use cases. • Sharing expertise and promoting good practices, standards and tools across the BI/DWH, data and AI teams, while contributing actively as a hands-on team member. • Staying abreast of relevant developments in data platforms, integration, analytics and AI-related data architecture, and assessing their practical applicability to the client.