Senior 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.

Your profile

• University degree (EQF 6: Bachelor's level or equivalent).
• Minimum 12 years of professional IT experience.
• Capacity to leverage storytelling in data architecture communications.
• Ability to synthesize long-term business objectives with technical feasibility to guide project vision and validate architectural decisions.
• Ability to understand, speak and write English
• Knowledge of French would be considered an asset.
• Ability to work in a team as well as autonomously.
• Ability to participate in multilingual meetings.
• Excellent interpersonal and communication skills.
• Results-oriented mindset, focused on delivering.

Technical skills

• Minimum 2 years’ experience in enterprise data architecture and platform assessment, including BI, data and integration environments.
• Minimum 2 years hands-on with enterprise relational databases/DWH, including SQL analysis, troubleshooting and performance optimization.
• Minimum 2 years hands-on with ETL/ELT and data integration tools, developing, adapting and troubleshooting pipelines.
• Minimum 2 years applying data modelling across conceptual, logical and physical layers, including 3NF, Data Vault, dimensional/star-schema approaches.
• Minimum 1 year involved in enterprise data/BI/DWH migrations.
• Strong experience in assessing data, data integration/ETL and reporting/BI platforms against concrete business and technical requirements, including analysis of functional fit, architecture, interoperability, performance, scalability, security, migration complexity and operational constraints.
• Proven experience in designing, preparing and supporting migrations of enterprise data and BI/DWH environments, including assessment of existing architectures, dependencies and interfaces; definition of target architectures and transition scenarios; data mapping; migration sequencing; validation, cutover and coexistence strategies.
• Strong hands-on experience with enterprise relational database and data warehouse environments, in particular SQL-based systems, including database-level analysis, troubleshooting, performance optimisation and data migration. Experience with Oracle environments is particularly relevant.
• Strong knowledge and practical experience with data integration patterns and tools, including ETL/ELT, data transformation, orchestration and batch-processing pipelines. Ability to analyse, develop, adapt and troubleshoot ETL/ELT processes directly is required.
• Strong knowledge of data modelling approaches, including relational/3NF and dimensional/star-schema modelling, and practical experience in creating and adapting conceptual, logical and physical data models for operational, analytical and migration purposes.
• Experience with enterprise BI and reporting architectures, including the data models, semantic/metrics layers and interfaces supporting dashboards and corporate reporting solutions. Experience with migration between BI/reporting platforms is an advantage.
• Knowledge of modern data platform and warehouse/lakehouse architectures and the ability to assess their applicability and migration implications in an enterprise environment, including interoperability with existing relational databases, data warehouses, ETL processes and BI solutions.
• Experience with relational data stores and knowledge of alternative data representations and stores, including NoSQL and graph-based approaches, with an understanding of when these provide concrete value.
• Knowledge of metadata management, data lineage, cataloguing, master/reference data and data quality practices, and their application to reliable integration, migration, traceability and reuse of enterprise data.
• Experience with data profiling, validation, reconciliation and quality controls, particularly in the context of data transformation and migration, to ensure completeness, consistency and integrity between source and target environments.
• Understanding of privacy, security and compliance requirements applicable to enterprise data platforms, including access control, encryption, data residency, backup/recovery and business-continuity considerations.
• Knowledge of API and interoperability standards and approaches for data access and system integration, including SQL and REST-based interfaces.
• Experience with DataOps and software-engineering practices relevant to data platforms, including version control, automated testing, deployment and environment promotion.
• Knowledge of enterprise data architecture methods, standards and documentation practices, with the ability to apply them pragmatically and proportionately rather than as an end in themselves.
• Understanding of the requirements for making structured and unstructured enterprise data suitable for AI and agentic use, including stable identifiers, entities and relationships, metadata, provenance and machine-usable semantic representations.
• Knowledge of semantic modelling, knowledge graphs and ontology-based approaches is an advantage, particularly where these can complement traditional relational and analytical data architectures and support AI use cases.
• Ability to work effectively across business, BI/DWH, data engineering and AI teams, translating between business requirements and technical implementation while contributing directly to practical technical work.

Certificates and standards:

• Mandatory: One of the following or an equivalent certification: TOGAF, CDMP, DAMA-DMBoK, and ISO data governance standards.

WHAT´S IN IT FOR YOU?

• We value your contribution, which is why we offer a competitive and attractive salary package.
• Your wellbeing is our priority – from day one, you are covered by a comprehensive health insurance plan.
• Benefit from a convenient meal allowance provided through a ticket restaurant card.
• Take part in impactful projects that make a difference at both the national and European level.
• Continue to grow with us through in-house training sessions and a wide range of online learning opportunities.
• Join a collaborative culture where we regularly celebrate achievements and milestones together.