Data Architect

About this role

As EcoVadis continues to evolve its business and expand its product portfolio, we are embedding AI more deeply into how we operate. The Enterprise Data Architect plays a critical role in sustaining the growth. Sitting at the intersection of business, data, and technology, you will bridge business strategy and technology strategy, acting as the technical "North Star," to evolve our Data Platform into a high-performance data mesh ecosystem that treats data as a product and AI as a core competency.

You will bridge the gap between traditional data modeling and the emerging needs of Generative AI, AI Agent consumption, LLM orchestration, and real-time MLOps. You will be the owner of the Data Strategy on the architecture side, supporting multiple teams including Analytics, BI, Data Engineering, Data Governance, Engineering and AI/ML teams to ensure our ecosystem remains scalable, resilient, and AI-enabled. Key Responsibilities Enterprise Data Strategy Building target architectures and long-term strategic roadmaps alongside Solutions, Engineering, Data and IT teams.

Become the owner of the Data Strategy on the architecture side, translating complex business needs into pragmatic, future-proof architectures. Define and govern Enterprise Data Models and Domains, expanding beyond traditional analytics to AI/ML workloads. Lead the architecture side of the Data Strategy, specifically designing for "AI-readiness" by integrating Feature Stores, Vector Databases and other technologies into our long-term roadmap.

Emerging Technologies Explore and validate new solutions in the data platform space based on business requirements or research. Perform technical Proof of Concepts, document them, and assist teams in industrializing them. Monitor new technological advances to assist teams in reducing technical debt and adopt the right tooling Data Governance & MLOps Collaborate with Data Governance and AI teams to: Continuously improve architecture governance practices.

Develop and maintain policies, standards, and guidelines to ensure a consistent framework is applied across the data platform. Identify discrepancies between the technical architecture, agreed practices, and system designs proposed by project teams. Ensure architectures adhere to data compliance frameworks (e.g., GDPR, CCPA) and ethical considerations in AI/ML model deployment. Collaboration & Data Product Enablement Collaborate with Product Managers and Data Product Owners to enable Data as a Product, ensuring they are designed for reusability, scalability, and self-service consumption.