Data Architect

EcoVadis

5d•Hybrid

About The Position

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.

Requirements

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. data mesh, medallion architecture, self-service analytics & BI.
Specific experience with Databricks is a must.
Experience with enterprise data models design, knowledge of various data modeling patterns, data engineering practices, data management standards i.e. DAMA.
At least 5 years focusing on architecture (any of: data architecture, solution architecture, enterprise architecture).
Broad understanding of technical aspects of data integration, including data catalogs, data quality, solution maintainability, performance and security.
Practical experience with concepts such as MDM, RDM, CDP, ETL, ELT, rETL, SQL, noSQL, near-real time analytics.
Approach going beyond only technical components of architecture, but rather including people - process - technology as a whole.
Experience with all stages of SDLC - from building vision & strategy, through solution design, vendor selection process in some projects, technical design, leading workshops, assisting implementation teams & troubleshooting issues and driving solution adoption.
Good stakeholder management skills; proficiency in verbal and written communication; facilitation skills.
Pragmatic approach to governance & application portfolio rationalization.
Data Compliance and Ethics: data compliance frameworks (e.g., GDPR, CCPA, SOX) and ethical considerations in data usage and AI/ML model deployment.
Must be a team player, but with a high degree of self-organization.
Ability to mentor and guide teams, fostering a culture of technical excellence and innovation.

Nice To Haves

Relevant certifications (i.e. CDMP/DAMA, TOGAF, CIPP) would be a plus.

Responsibilities

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.
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.
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.
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.
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.