Data Architect

Onesource Consulting

Brussel, Belgium

1 day ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.adzuna.be

Tech stack

Geographic Information Systems Application Programming Interfaces (APIs) Artificial Intelligence Microsoft Azure Software as a Service Continuous Integration Data Architecture Data Governance Data Hub Data Infrastructure Extract Transform Load (ETL) Data Vault Modeling

+18 more

Dimensional Modeling Role-Based Access Control Power BI Azure DevOps Pipelines Azure Data Lake Service Layer Digital Twin Real Time Systems Azure Data Factory Delivery Pipeline Technical Debt Infrastructure as Code (IaC) AI Platforms Machine Learning Operations Terraform Azure Synapse Analytics Key Vault Databricks

Job description

Contribute to the implementation of the Azure Data Architecture used by the City of Brussels;
Contribute to the City of Brussels’ Digital Twins project focused on heat islands;
Assist on the hands-on of the implementation of CI/CD around the Data platform in Azure., * Have you designed and delivered a Lakehouse platform on Azure Databricks, including multiple layers of governance: Unity Catalog for access control, and a Microsoft Purview-like enterprise catalog for cataloging, classification, and lineage?
Have you deployed an Azure data/AI platform entirely via Infrastructure as Code (Terraform or equivalent), industrialized in Azure DevOps CI/CD pipelines, with network isolation (private endpoints, VNet integration) and Entra ID RBAC model?
Have you designed an architecture for real-time ingestion of IoT sensor streams and geospatial data powering a digital twin or smart city application?
Have you designed, deployed and operated the AI/ML layer of a data platform in production, from model development to industrialization (MLOps), by integrating it with the existing ingestion and governance layers?
Have you upgraded the data model of a platform in production (Data Vault, dimensional models on Medallion architecture) as part of a high-risk migration (change of catalog or technical base), while maintaining continuity of service?
End-to-end data architecture and value: Describe a data platform that you have architected end-to-end on Azure, from ingestion to value. Specify the chosen data model (medallion, Data Vault, dimensional models) and the reasons for this choice, the incremental ingestion and orchestration strategy, and the design of the service layer towards reporting (Power BI semantic models or equivalent, and associated security model). Point to an architectural arbitration that you have decided and what you would do differently today.
Digital twin and sensor flow: Explain how you design the complete chain between field sensors and an urban digital twin. Deal with the semantic standardization of context data and the choice of urban data models, the integration of heterogeneous sources (IoT, geospatial, BIM, open data), the management of latency and measurement quality, and the articulation between real-time processing and the historical analytical layer serving analysis and prediction.
Architecture AI/ML layer: Describe the AI/ML layer of a data architecture that you have designed and put into production. Specify the place of the training, model registry, inference, and monitoring components in the target architecture, the predictive use case being addressed and the reasons for the model choice, how the infrastructure of this layer is provisioned and promoted across environments, and how the models inherit the platform’s governance and access controls. Indicate how you monitor drift and trigger a reworkout.
Industrialization, security and migrations: Describe your approach to the lifecycle of the platform’s components: landing zones, provisioning blueprints, environment segregation, private agents and execution pools, testing and promotion strategy, secrets and policy management. Illustrate with a high-risk migration you have conducted by detailing the preparation, failover plan, checkpoints, and service disruption control.
Architect role and skills transfer: Describe how you position the role of architect between internal teams, external partners and business lines: definition and application of standards, architecture reviews, technical debt management, documentation, skills development of internal teams. Give a concrete example of a technical disagreement with a partner or supplier and how you resolved it.

Requirements

Data repository architecture (MDM/EDM), exchanges via ESB/API.
Azure DevOps and Infrastructure as Code (IaC) to manage the lifecycle of data platform components (Terraform or equivalent).
Design and urbanization of end-to-end data architectures for the ingestion, real-time processing and valorization of IoT sensor flows for digital twins.
Data Platform Azure et SaaS Microsoft (Azure Data Lake, Azure Data Factory, Azure Synapse Analytics, Azure Databricks)
Digital Twin and integration of geospatial/IoT/BIM data (Smart City context).
AI/ML and integration into a modern data architecture (MLOps, advanced analytics platforms)
Microsoft Purview (Data Governance): cataloguing, lineage, classification, data quality.
Power BI.
ELT / ETL processes, data modeling (Lakehouse, medallion architecture, dimensional models).
Security of cloud data platforms: RBAC, private endpoints, networking, Key Vault, policies.