Data Engineer Architect

Your Role

• Data Architect Data Governance, Data Modelling & Databricks
• Define and implement enterprise data architecture, governance frameworks, and target-state data platforms leveraging the Databricks Lakehouse architecture.
• Design and maintain conceptual, logical, and physical data models across business domains, ensuring scalability, consistency, performance, and reusability.
• Establish and enforce data governance policies covering data ownership, stewardship, data quality, metadata management, lineage, privacy, and lifecycle management.
• Architect and govern enterprise data solutions using Databricks, Delta Lake, Unity Catalog, and cloud-native services on Azure, AWS, or GCP.
• Lead data modelling initiatives using dimensional modelling, Data Vault, canonical models, and normalized data structures for operational and analytical workloads.

Your Profile

• Implement and oversee Master Data Management (MDM), reference data management, and enterprise data catalog capabilities to improve data trust and accessibility.
• Define data quality standards, validation frameworks, monitoring processes, and remediation strategies to ensure high-quality, business-ready data.
• Drive metadata management and end-to-end data lineage capabilities using Databricks Unity Catalog, Microsoft Purview, Collibra, or similar governance platforms.
• Partner with data engineering, analytics, AI/ML, and business teams to establish standardized data products, reusable data assets, and governed self-service analytics.
• Design secure and compliant data architectures, incorporating role-based access control, data classification, masking, encryption, and regulatory compliance requirements.
• Establish architecture standards and best practices for data ingestion, transformation, orchestration, and consumption using Databricks, Spark, Delta Live Tables, and workflow automation tools.
• Mentor architects, data engineers, and data stewards on enterprise data governance, modelling standards, Lakehouse architecture patterns, and Databricks best practices.