AI-Driven Data Architect: Lead Data Mesh

Company Description Work smart, have fun and make an impact EcoVadis is the leading provider of business sustainability ratings. Our solutions are backed by an international team of experts and powerful technology. We analyze data and build sustainability scorecards that give companies actionable insights into their environmental, social and ethical risks. Why apply to EcoVadis? Be a part of the global sustainability change in business. Grow your career. Work with extraordinary people. Feel valued for your contribution. Learn more about our team and culture on EcoVadis careers page. Job Description 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. Define best practices for the development, maintenance, and lifecycle management of data products. Act as a hands-on mentor to Data Engineering and Analytics teams, guiding them on day-to-day data modeling patterns, best practices, and execution choices. Advise business and technical stakeholders on self-service data platform capabilities to maximize value. Partner with IT team members to ensure architecture aligns with security strategy and policies. Operational Excellence Drive continuous improvement and help drive the adoption of new data tools and practices. Apply a pragmatic approach to governance and application portfolio rationalization. Be an active part of the D&A leadership team. Personal Attributes High level of proactivity and autonomy Strong planning, prioritization, and

organizational skills Pragmatic mindset with a focus on evolution over big-bang transformations Comfortable challenging assumptions while remaining collaborative Must be a team player with a high degree of self-organization. Good stakeholder management skills, proficiency in communication, and strong facilitation skills. Approach architecture by going beyond technical components to include people, process, and technology as a whole. Qualifications Very good knowledge of data platforms in general, i.e. data warehouses, data lakes, lakehouses, BI tools, embedded analytics solutions. Expertise in multi-domain analytical architectures, i.e.