1. Enterprise Data Architecture • Define and maintain the enterprise data architecture strategy, principles, standards, and reference architectures. • Design scalable Enterprise Data Platform (EDP) architectures supporting analytics, reporting, AI/ML, and enterprise applications. • Develop conceptual, logical, and physical data architectures and data models. • Define data architecture patterns covering data ingestion, integration, storage, processing, serving, analytics, and consumption. • Establish standards for data modeling, data integration, data storage, data lifecycle, and data interoperability. • Ensure architecture decisions align with enterprise technology strategy and business objectives. • Conduct architecture assessments and identify opportunities for modernization and optimization. 2. Cloud Data Platform Architecture • Architect and govern cloud-based data platforms using technologies such as: • Microsoft Azure • Google Cloud Platform (GCP) • Databricks • Define architecture patterns for data lakes, data warehouses, lakehouses, and modern data platforms. • Evaluate cloud data services and recommend appropriate technologies based on scalability, performance, cost, security, and business requirements. • Ensure cloud data platforms are designed for high availability, scalability, resilience, performance, and cost optimization. • Establish standards for cloud data platform deployment and operationalization. 3. Data Governance • Define, implement, and enforce enterprise-wide Data Governance frameworks, policies, standards, and operating models. • Establish governance processes for data ownership, stewardship, classification, access, usage, retention, and lifecycle management. • Define and enforce data architecture and governance standards across projects and implementation partners. • Establish processes for managing critical data assets and business-critical datasets. • Ensure governance practices align with organizational security, privacy, regulatory, and compliance requirements. 4. Metadata, Lineage & Data Catalog • Define enterprise strategies for Metadata Management, Data Cataloging, and Data Lineage. • Establish standards for technical, business, and operational metadata. • Ensure end-to-end visibility of data lineage across source systems, data platforms, transformations, and consumption layers. • Support implementation and adoption of enterprise data catalog and metadata management solutions. • Improve data discoverability, traceability, and understanding across the organization. 5. Data Quality Management • Define enterprise Data Quality frameworks, standards, KPIs, and processes. • Establish data quality rules covering accuracy, completeness, consistency, timeliness, uniqueness, and validity. • Implement mechanisms for continuous monitoring and reporting of data quality. • Work with business data owners and technology teams to identify and resolve critical data quality issues. • Ensure data quality requirements are incorporated into data platform and integration designs. 6. Architecture Review & Solution Governance • Lead and conduct architecture reviews for data platform and transformation initiatives. • Review solution designs, technical architecture documents, data models, integration designs, and technology selections. • Validate proposed solutions against enterprise architecture, security, governance, performance, and scalability standards. • Identify architectural risks, dependencies, gaps, and opportunities for improvement. • Provide architecture guidance and technical direction to engineering teams and implementation partners. • Ensure deviations from enterprise standards are properly assessed, documented, and approved. 7. Enterprise Transformation & Modernization • Provide data architecture leadership for large-scale Enterprise Transformation, Data Modernization, Analytics, AI/ML, and ERP modernization programs. • Translate business requirements into scalable enterprise data architecture solutions. • Work with business and technology leadership to define target-state architecture and transformation roadmaps. • Support migration from legacy data platforms to modern cloud-based architectures. • Ensure data architecture supports future AI, GenAI, analytics, and digital transformation initiatives. 8. Security, Compliance & Risk • Ensure enterprise data architectures adhere to security, privacy, regulatory, and compliance requirements. • Define appropriate approaches for data access control, encryption, data classification, and secure data sharing. • Work closely with security and risk teams to address data-related security requirements. • Identify and mitigate architecture and data governance risks. • Ensure sensitive and critical data is appropriately protected throughout its lifecycle. 9. Stakeholder & Partner Management • Collaborate with business leaders, enterprise architects, data engineering teams, security teams, IT teams, and implementation partners. • Facilitate architecture workshops and design discussions with senior stakeholders. • Communicate complex technical concepts clearly to both technical and non-technical audiences. • Provide technical leadership and direction to internal teams and external implementation partners. • Drive alignment between business objectives, technology strategy, and data architecture. • Manage architecture-related dependencies, risks, and escalations across enterprise programs. Required Technical Skills Must-Have • Strong experience in Enterprise Data Architecture. • Strong knowledge of Data Modeling – conceptual, logical, and physical. • Experience designing and governing Enterprise Data Platforms. • Strong experience with one or more cloud data platforms: • Azure • GCP • Databricks • Strong understanding of Data Governance frameworks and operating models. • Experience with Data Quality frameworks and management. • Strong knowledge of Metadata Management, Data Catalog, and Data Lineage. • Strong understanding of Data Lake, Data Warehouse, and Lakehouse architectures. • Experience with enterprise-scale data integration and ETL/ELT architectures. • Strong understanding of Cloud Architecture, Data Security, and Compliance. • Experience leading architecture reviews and solution design governance. • Strong stakeholder management and communication skills. Good to Have • Experience with Azure Data Factory / Synapse / Microsoft Fabric. • Experience with Databricks Lakehouse architecture. • Experience with GCP BigQuery / Dataflow / Dataproc. • Experience with enterprise Data Catalog / Metadata tools such as Microsoft Purview, Collibra, Alation, or equivalent. • Knowledge of Master Data Management (MDM). • Experience with Data Mesh / Data Fabric concepts. • Knowledge of API-led integration and event-driven architectures. • Exposure to AI/ML and Generative AI data architectures. • Experience with large-scale ERP modernization or cloud migration programs. Leadership & Behavioral Competencies • Strong architecture leadership and decision-making ability. • Excellent stakeholder management and communication skills. • Ability to influence architecture decisions across multiple teams and business functions. • Strong analytical and problem-solving skills. • Ability to work effectively with senior leadership, architects, engineering teams, vendors, and system integrators. • Ability to manage multiple enterprise initiatives and competing priorities. • Strong understanding of business requirements and ability to translate them into technology solutions. • Comfortable working in complex, global, and transformation-driven environments.