Enterprise Data Architect

As the Lead Enterprise Architect for Data & AI, you will be responsible for defining and driving an enterprise-wide data and information architecture strategy. Your role will ensure data platforms, governance frameworks, and information assets are fully aligned with the needs of business domains such as Commercial, Finance, Supply Chain, Product, and Regulatory. You will lead the design of a scalable, trusted, and connected data architecture that enables AI/ML, advanced analytics, digital transformation, and regulatory compliance. This is a critical leadership role in advancing the organization’s data-driven enterprise vision.

Core Responsibilities of the Enterprise Architect Role:

• Bridge alignment between business and IT across a federated technology environment.
• Build strong stakeholder relationships with business and IT leaders.
• Respond to evolving business models and operating landscapes.
• Evaluate emerging trends and disruptions and assess their enterprise implications.
• Visualize future-state architectures to influence long-term business planning.
• Operate across multiple delivery models, including product- and project-centric environments.
• Communicate the value of enterprise architecture and grow its organizational influence.
• Continuously evolve the enterprise architecture practice and service portfolio.
• Mentor architects, product managers, and business leaders to embed architectural thinking.

Principal Accountabilities:

• Define the enterprise data architecture vision, target state, and guiding principles, aligned with business priorities and regulatory frameworks.
• Lead architecture for enterprise data platforms such as Azure Synapse, Databricks, Power BI, and Informatica.
• Establish enterprise-wide standards for master data, metadata, lineage, and data stewardship.
• Collaborate with business and domain architects to identify and support key data domains.
• Provide architectural oversight for major initiatives in data ingestion, transformation, and analytics.
• Define data access, privacy, quality, and lifecycle management policies at the enterprise level.
• Champion enterprise taxonomies, data product strategies, and federated governance.
• Stay informed on and assess innovations such as data mesh, lakehouse, and generative AI for enterprise applicability.

Key Decision Areas:

• Defining the balance between centralized vs. federated data ownership and governance models.
• Selecting appropriate architecture patterns (e.g., data lakehouse vs. warehouse) for enterprise-wide data needs.
• Prioritising foundational governance work vs. immediate business-driven data initiatives.
• Defining abstraction levels for enterprise data models and ontologies.
• Recommending scalable architectures for AI/ML workloads and real-time data streaming.

Skills & Experience

Essential:

• Significant experience in enterprise architecture with a strong focus on data, information, or analytics.
• Proven hands-on expertise with data platforms such as Azure Data Lake, Synapse, Databricks, Power BI, etc.
• Deep knowledge of data governance, MDM, metadata management, and data quality frameworks.
• Understanding of data protection and privacy regulations (e.g., GDPR, CCPA).
• Track record of developing and executing enterprise data strategies at scale.
• Strong communication, stakeholder engagement, and executive influencing skills.
• Skilled in collaborating across departments, resolving conflicts, and driving shared outcomes.
• Comfortable working with senior executives and pushing back diplomatically when needed.
• Analytical and problem-solving mindset with a focus on long-term value creation.
• Strong delivery orientation, adaptability, and comfort with ambiguity.
• Multicultural awareness and professional integrity.
• Familiarity with enterprise architecture tools (e.g., LeanIX).

Desirable:

• Experience with data governance tools like Collibra, Informatica Axon/EDC.
• Knowledge of advanced data architecture concepts (e.g., data mesh, data fabric, domain-oriented design).
• Familiarity with data science and AI/ML platforms and their integration into enterprise strategies.
• Experience working in highly regulated, global environments (e.g., FMCG, finance, life sciences).
• Skilled in developing architecture models, roadmaps, strategies, and principles.
• Knowledge of architecture notations and modeling techniques.
• Experience with enterprise-scale transformation initiatives.
• Hands-on application of frameworks such as TOGAF or Zachman.
• Deep expertise in at least one architecture domain (e.g., data, applications, security, integration); good working knowledge of others.
• Excellent facilitation, negotiation, and conflict resolution skills.
• Educated to degree level (or equivalent professional experience); MBA or postgraduate diploma preferred.