Data Architect

Job Purpose

Key Accountabilities

• Design and govern enterprise data architecture frameworks aligned with business and technology strategies.
• Identify and define the key components of the enterprise data landscape.
• Design end-to-end data platform architectures supporting analytics, AI, reporting, and operational use cases.
• Develop and maintain conceptual, logical, and physical data models.
• Ensure data models align with business semantics, performance requirements, scalability, and data quality standards.
• Design scalable and modular data architectures to support current and future business needs.
• Collaborate with business stakeholders to translate business objectives into data requirements, business rules, and transformation logic.
• Define source-to-target mappings and data transformation specifications.
• Perform source system analysis and identify extraction, integration, and ingestion requirements.
• Design data flows across data warehouses, data lakes, lakehouses, and analytical platforms.
• Prepare high-level and detailed solution design documents, architecture artifacts, and modeling deliverables.
• Ensure compliance with data governance standards, metadata requirements, lineage principles, and enterprise architecture guidelines.

Minimum Qualifications

• Bachelor's Degree in Computer Science, Information Technology, Engineering, Information Systems, or Data Science.
• Professional certifications in Data Architecture, Data Modeling, Data Management, or Enterprise Data Management.
• CDMP (Certified Data Management Professional) or equivalent certification.

Minimum Experience

• 6+ years of experience in Data Architecture, Data Modeling, Data Design, Data Engineering, or Enterprise Data Management.
• Proven experience designing enterprise data platforms and analytical environments.
• Experience working with large-scale data transformation, analytics, and AI initiatives.

Job-Specific Skills

• Enterprise Data Architecture, Data Platform Architecture, Data Warehouse Architecture, Data Lake Architecture, and Lakehouse Architecture.
• Data Integration Architecture, Data Flow Design, and Source-to-Target Mapping.
• Conceptual, Logical, and Physical Data Modeling; Dimensional Modeling; Master and Reference Data Modeling.
• Normalization and Denormalization Techniques; Slowly Changing Dimensions (SCD).
• Business Requirements Analysis, Source System Analysis, and Data Mapping and Transformation Design.
• Data Governance Principles, Data Lineage and Traceability, Metadata Management Awareness, and Data Quality Requirements.
• Erwin Data Modeler, Microsoft Visio, SQL and Database Technologies, Data Warehousing Platforms, and ETL/ELT Concepts.