Data Architect (Contractual)

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.