Enterprise Data Architect

We are seeking an experienced Enterprise Data Architect to lead the design and implementation of scalable, enterprise-grade data platforms. This role will focus on defining data architecture strategy, governance, and modern data solutions leveraging Azure Databricks and Lakehouse architecture, with a strong emphasis on AI/ML integration. The ideal candidate will have a strong background in enterprise data architecture, cloud data platforms, and AI/ML-driven data solutions, with the ability to work closely with business and technical stakeholders to deliver high-quality, scalable data products. Key Responsibilities: • Define and implement enterprise data architecture strategy and roadmap • Design scalable data platforms using Azure Databricks and Lakehouse architecture • Lead the integration of AI/ML models and data pipelines into enterprise data platforms • Establish and enforce data governance, data quality, and security frameworks • Collaborate with data engineers, data scientists, and business stakeholders to translate requirements into data solutions • Define standards for data modeling, data integration, and data lifecycle management • Drive cloud data platform modernization initiatives • Provide architectural guidance on performance, scalability, and cost optimization • Ensure alignment with enterprise architecture standards and best practices Required Skills: • 15+ years of overall IT experience • 7+ years of experience as a Data Architect / Enterprise Data Architect (MANDATORY) • Strong expertise in Azure Databricks, Delta Lake, and Lakehouse architecture • Mandatory experience with AI/ML (model development, integration, or MLOps) • Strong knowledge of data modeling (Dimensional, Data Vault, etc.) • Experience with Azure data services (ADF, ADLS, Synapse, etc.) • Strong understanding of data governance, security, and compliance frameworks • Experience designing large-scale, distributed data systems • Proficiency in SQL, Python, and Spark-based technologies • Experience with CI/CD pipelines and DevOps practices