Enterprise Architect (Data & AI)

Soprasteria1; Brussel; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Soprasteria is seeking an Enterprise Architect specializing in Data & AI to shape the organisation's data and AI landscape. You will define the architecture vision for data, analytics, AI, and Generative AI and ensure scalable, secure, and well-governed platforms aligned with business priorities.

You will bridge business strategy with architecture principles, target-state roadmaps, and guidance for delivery teams, collaborating with data engineering, analytics, and AI specialists to deliver

Qualifications

• Extensive experience in data architecture, solution architecture, or enterprise architecture leadership.
• Experience governing large-scale data platforms (data lakes/warehouses/lakehouses).
• Strong understanding of data modelling: dimensional modelling, Data Vault, data mesh/fabric.
• Knowledge of AI/ML lifecycle, deployment, monitoring, and MLOps practices.
• Familiarity with Generative AI, LLM architectures, RAG, model orchestration.

Responsabilités

• Define and evolve the enterprise Data & AI architecture vision and roadmaps.
• Establish standards for data modelling, governance, data quality, metadata, and lifecycle management.
• Design architectures for modern data platforms across cloud, hybrid, and on-premises.
• Define architecture patterns for AI/ML capabilities and Generative AI governance.

Connaissances

Data architecture

Enterprise architecture

Cloud platforms

AI/ML lifecycle

MLOps

Generative AI

Data governance

TOGAF/Archimate

Outils

Databricks

Snowflake

Azure

AWS

Google Cloud

Description du poste

We are looking for an Enterprise Architect specializing in Data & AI to join our architecture team and help shape our enterprise-wide data and AI landscape.

In this role, you will define and evolve the architecture vision for data, analytics, AI, and Generative AI, ensuring that platforms and capabilities are scalable, secure, well-governed, and aligned with business priorities.

You will work at the intersection of business strategy, enterprise architecture, data engineering, and AI/ML , translating strategic objectives into architecture principles, target-state architectures, technology roadmaps, and practical guidance for delivery teams.

Key Responsibilities

• Vision & Roadmap: Define and evolve the enterprise Data & AI architecture vision, including target-state architectures, reference architectures, architecture principles, and technology roadmaps.
• Data Architecture & Standards: Establish and promote standards for data modelling, governance, master data management, data quality, metadata management, and data lifecycle management.
• Data Platforms: Design and validate architectures for modern data platforms, including data lakes, data warehouses, lakehouses, and streaming solutions across cloud, hybrid, and where relevant on-premises environments.
• AI & ML Architecture: Define architecture patterns for AI and ML capabilities, including MLOps, model lifecycle management, model monitoring, and integration with enterprise data platforms.
• Generative AI: Define scalable and secure architecture patterns for Generative AI and LLM-based solutions, including RAG, enterprise data integration, model access, orchestration, and governance.
• Business Alignment: Partner with business and technology stakeholders to translate strategic objectives and use cases into Data & AI capabilities and architecture roadmaps.
• Architecture Governance: Guide project and engineering teams on architecture decisions, facilitate architecture reviews, and promote alignment with enterprise standards and design principles.
• Technology Evaluation: Assess technologies, platforms, and vendors across cloud, data, analytics, AI, and machine learning, balancing business value, technical fit, security, cost, and long-term sustainability.
• Data & AI Governance: Ensure that privacy, security, responsible AI, and applicable regulatory requirements are considered throughout architecture and design decisions.
• Responsible AI: Help define architecture guardrails around model risk, transparency, explainability, monitoring, and responsible use of AI.
• Technical Leadership: Act as a trusted architecture advisor to data engineering, analytics, AI, and data science teams, sharing best practices and supporting complex architecture decisions.
• Enterprise Collaboration: Work closely with fellow enterprise architects across applications, infrastructure, security, integration, and cloud to ensure architectural coherence.
• Innovation & Technology Watch: Stay informed about developments in data platforms, AI/ML, and Generative AI and assess where emerging technologies can create meaningful value for the organisation.

Your Experience & Expertise

• Extensive experience in data architecture, solution architecture, enterprise architecture, or comparable technical leadership roles , with strong exposure to Data & AI.
• Proven experience designing or governing large-scale data platforms , such as data lakes, data warehouses, lakehouse architectures, or equivalent modern data ecosystems.
• Strong understanding of data modelling and architecture approaches , such as dimensional modelling, Data Vault, domain-driven data design, data mesh, or data fabric.
• Good understanding of the AI/ML lifecycle , including model development, deployment, monitoring, and MLOps practices.
• Familiarity with Generative AI and LLM architectures , including concepts such as Retrieval-Augmented Generation (RAG), vector databases, model orchestration, and LLMOps.
• Experience with one or more major cloud Data & AI ecosystems , such as Microsoft Azure, AWS, or Google Cloud, and/or platforms such as Databricks or Snowflake.
• Strong understanding of data governance, metadata management, data cataloguing, and data quality .
• Familiarity with enterprise architecture frameworks and modelling approaches such as TOGAF, Zachman, or ArchiMate .
• Understanding of security, privacy, and regulatory considerations for Data & AI , including areas such as GDPR and the EU AI Act.
• Strong communication and stakeholder management skills, with the ability to engage effectively with both technical teams and senior business stakeholders .

Certifications

Relevant certifications are considered an advantage, for example:

• TOGAF
• ArchiMate
• Databricks certifications
• Microsoft Azure Data or AI certifications
• AWS Data or Machine Learning certifications
• Google Cloud Data or Machine Learning certifications

Equivalent expertise demonstrated through professional experience is equally valued.

What Will Help You Succeed

You can translate business strategy i