Data Architect

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. SAP & Enterprise System Integration

• Define architecture for integrating data from SAP and other enterprise applications into the Enterprise Data Platform.
• Work with SAP, ERP, CRM, and other enterprise system teams to understand source systems and data structures.
• Design robust batch and real-time data integration patterns.
• Ensure data integration architectures support enterprise transformation and ERP modernization initiatives.
• Evaluate integration approaches, interfaces, APIs, ETL/ELT pipelines, and data exchange mechanisms.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.
• Experience with SAP data and enterprise system integrations.
• 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.
• Knowledge of SAP S/4HANA and SAP data integration.
• 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.