DATA ARCHITECT

Responsibilities
• 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/or STANDARDS
• Mandatory: One of the following or an equivalent certification: TOGAF, CDMP, DAMA-DMBoK, and ISO data governance standards.
• Optional: Cloud data certifications (AWS, Azure, etc.), ITIL 4 Foundation or SAFe, and security certifications (e.g., CCSP, CISSP). Knowledge and skills
• 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, optionally French as an additional 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.