Data Architect

Key Responsibilities

• Define, evolve, and document the organization’s data architecture aligned with business and IT strategy.
• Design enterprise data models (conceptual, logical, and physical), establishing naming conventions and modeling standards.
• Audit and recommend data technologies (Data Lake, Lakehouse, Mesh, Warehouse) based on evolving business needs.
• Define policies and standards for data governance, quality, privacy, cataloging, and lineage.
• Lead adoption of metadata management and data discovery tools across teams.
• Ensure compliance with internal and external data regulations and security requirements.
• Architect data integration solutions (ETL/ELT, real-time and batch pipelines).
• Ensure interoperability across domains, sources, and consumers using principles such as Data Mesh.
• Define integration patterns and data federation frameworks to deliver a 360° data view.
• Collaborate with engineering, analytics, and business teams to act as a technical reference in data architecture.
• Promote adoption of data models, standards, and best practices across the organization.
• Translate business needs into scalable data solutions and facilitate technical-business alignment.

Required Experience

• 10+ years of experience in Data Engineering.
• 3+ years designing cloud-based data architectures (Azure, AWS, or GCP).
• 2+ years in data architecture, enterprise data modeling, or data governance.
• Led data model design (relational, multidimensional, non-relational) for Data Warehouse, Data Lake, or Lakehouse architectures.
• Participated in multi-source data integration projects (on-premise, cloud, external sources).
• In-depth knowledge of data governance frameworks including quality, cataloging, privacy, and compliance.
• Experience with modern architectures (Data Mesh, Lakehouse) and cataloging tools (Purview, Unity Catalog) is a plus.

Technical Expertise

• Data Modeling: Conceptual, logical, physical modeling; normalization; relational and non-relational design.
• Architectures: Data Warehouse, Data Lake, Lakehouse, Data Mesh.
• Governance: Data lineage, quality, privacy, RBAC, metadata management.
• Platforms: Azure Synapse, Azure Data Lake Gen2, Purview, Unity Catalog, Cosmos DB.
• Data Integration: Azure Data Factory, API Management, integration patterns, Azure Databricks.
• Infrastructure as Code (IaC): Terraform, Azure DevOps (preferred).
• Languages & Tools: SQL, Python (architectural level), JSON, Java, Scala, CI/CD: Git, Sonar, DevOps best practices.