Role Summary
The Lead Data Architect is responsible for designing, governing, and guiding enterprise-wide data architectures, ensuring scalable, secure, highquality, and compliant data foundations across the organization. As a senior technical leader and people manager within the Data & AI Architecture group, the role anchors the organization's data-first transformation while actively contributing to the definition and evolution of the enterprise AI architecture framework. The Lead Data Architect partners closely with AI architects, engineering, governance, and risk teams to ensure data platforms, data products, and integration patterns are AIready and capable of supporting advanced analytics and AI / GenAI solutioning. This position works crossfunctionally to accelerate value delivery while upholding reliability, compliance, and enterprise data standards.
Key Responsibilities
Data Architecture Leadership
- Define, maintain, and evolve the enterprise data architecture blueprint, including data platforms, data products, integration patterns, data models, and data flows that support analytics, AI, and Generative AI use cases.
- Provide technical leadership and recommendations on the selection and standardization of data platforms, tooling, and architectural patterns, ensuring scalability, security, data quality, and compliance with enterprise data governance standards.
- Lead architectural design reviews and governance forums to ensure data solutions align with enterprise architecture principles, security standards, regulatory requirements, and data management policies, while enabling AIready and GenAIready data foundations.
- Establish and embed data architecture solutioning into enterprise delivery and AICoE/DSAG processes, ensuring timely, AIready data foundations from early design through execution while accelerating delivery in alignment with governance, security, and regulatory expectations.
Solution Design & Delivery
- Lead endtoend data solution architecture across the data lifecycle - from source onboarding and integration to data serving and consumption - ensuring scalable, secure, and reusable designs aligned with enterprise standards.
- Guide teams on reusable data architecture patterns and components, including data platforms, domainaligned data products, canonical and semantic models, integration and streaming patterns, and data access layers.
- Ensure data products are designed for reuse (clear contracts, discoverability, and interoperability) and monitor adoption/usage of existing data products to promote reuse before build, reduce duplication, and improve timetovalue.
- Provide technical oversight to resolve complex issues across performance, data quality, lineage, and integration dependencies, in collaboration with engineering, governance, and platform teams.
- Be accountable for the data architecture team's delivery, ensuring outputs are delivered on scope, on budget, at required quality, and within agreed SLAs.
- Contribute to AI and GenAI solution design where required, reflecting the ongoing convergence of data and AI architecture capabilities during the group's transformation.
- Continuously evaluate emerging data management (with AI) platforms and technologies to enable adoption and facilitate modernization plans. This includes pilot activities that advance Data and AI Maturity.
Governance, Risk, & Compliance
- Establish and maintain architectural standards and guardrails for data management across the data lifecycle, including data quality, lineage, documentation, privacy, and appropriate use.
- Partner with Data Governance, Cybersecurity, Privacy, and Risk teams to ensure data solutions comply with regulatory requirements, internal controls, and enterprise risk frameworks.
- Ensure adherence to enterprise data governance frameworks and best practices through architecture reviews, design guidance, and ongoing oversight.
Stakeholder & Cross-Functional Collaboration
- Collaborate with business units to translate data strategy into technical architecture and roadmaps.
- Work with Engineering, Cloud, Security, and Data teams to ensure seamless integration of AI systems into enterprise platforms.
- Participate in Architecture Review Boards and technical councils as a subject matter expert in AI & ML.
Leadership & Team Development
- Lead and mentor a team of AI and data architects, providing technical guidance and career development.
- Foster a culture of innovation, knowledge-sharing, experimentation, and continuous improvement.
- Support resource planning, capability development, and recruitment for the AI architecture function.
Stakeholder & Cross-Functional Collaboration
- Partner with business units to translate data strategy and priorities into enterprise data architectures and roadmaps that support analytics, AI, and GenAI use cases.
- Work closely with Engineering, Cloud, Security, and AI teams to ensure data platforms and data products integrate effectively with enterprise and AI solutions.
- Participate in Architecture Review Boards and technical councils as a subject matter expert in data architecture, contributing to crossdomain architectural decisions.
Leadership & Team Development
- Lead and mentor a team of AI and data architects, providing technical guidance and career development.
- Foster a culture of strong data foundations, reuse, and continuous improvement while supporting the group's AI transformation.
- Support resource planning, capability development, and recruitment for the AI architecture function.
Qualifications
- Bachelor's degree in a relevant field; cloud and data certifications are an advantage.
- 7+ years in data or architecture roles, with at least 3 years in senior or lead roles; experience in regulated industries preferred.
- Strong expertise in data platforms, data modeling, integration patterns, metadata, data governance, and AIready data architectures.
- Strong leadership, communication, strategic thinking, and analytical skills.
- Experience designing enterprise data platforms and reusable data products that support analytics and AI/GenAI workloads, including modern data platforms, streaming, data product design, and cataloging practices, is preferred.
Success Metrics
- Continued refinement and optimization of scalable, secure, and trusted enterprise data architecture foundations.
- Increased reuse and adoption of standardized data products and patterns.
- Strong compliance with data governance, privacy, and regulatory requirements.
- High stakeholder satisfaction across delivery and business teams.
- Measurable growth in data architecture team capability and maturity.
