Building target architectures and long-term strategic roadmaps alongside Solutions, Engineering, Data and IT teams.
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.
Design 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.
Ensure architectures adhere to data compliance frameworks (e.g., GDPR, CCPA) and ethical considerations in AI/ML model deployment.
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.

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.
Good stakeholder management skills; proficiency in verbal and written communication; facilitation skills.
Ability to mentor and guide teams, fostering a culture of technical excellence and innovation.
Relevant certifications (i.e. CDMP/DAMA, TOGAF, CIPP) would be a plus.

Data Architect

Key skills