Leadership Responsibilities & capabilities
- Large matrix team leadership across domain Product Leads, Tech Leads and SMEs (~10)
- 1-2 CDT senior stakeholders Engine Directors
- 3-5 D&T senior stakeholders Consumer & Customer EA, other EAs and domain specific stakeholders
Purpose of Role
The Martech Solution Architecture Analytics & AI Lead ensures that Diageo s marketing and consumer analytics technologies are architected to be secure, scalable, and innovative, enabling actionable insights, decision intelligence, and performance optimisation.
This role drives architecture excellence across analytics domains including Consumer Planning and Insights, Digital Pulse of Consumer, Marketing Effectiveness and Media Optimisation, Experiential, Consumer Engagement, Commerce Experience, Digital Monitoring and Performance.
They will also lead the design and implementation of AI and GenAI solution architectures across all Martech domains, ensuring consistency, compliance, and forward-looking innovation.
Top Accountabilities
Technology Roadmaps, Architecture & Design
- Define and maintain domain-specific architecture roadmaps across analytics and AI, aligned with business priorities and enterprise standards.
- Lead the definition and documentation of architecture supporting advanced analytics, real-time dashboards, marketing KPIs, and predictive models.
- Translate business and marketing requirements into AI/analytics architecture patterns, technical solutions, and implementation blueprints.
- Develop architecture patterns and reference implementations for responsible, compliant and scalable AI and GenAI use cases across Martech.
System Integration & Data Flows - Architect data pipelines and models across Martech domains and enterprise systems to enable real-time insights, experimentation and performance tracking.
- Collaborate with data engineering teams to deliver a connected view of the consumer and marketing activity across platforms.
Tool Evaluation & Stack Rationalization - Evaluate and recommend analytics platforms (BI, experimentation, customer intelligence) and AI solutions with fit-for-purpose capabilities.
- Maintain strategic relationships with key analytics/AI vendors, contributing to their product roadmaps.
- Identify opportunities to consolidate or sunset legacy tools and reduce stack complexity.
Governance, Privacy & Compliance
- Implement AI ethics, data privacy, and responsible use frameworks across AI/analytics solutions.
- Partner with Legal, Security and Compliance to ensure adherence to data protection regulations (e.g., GDPR, CCPA).
Cross-Functional Collaboration - Partner with Product, Data Science, Media, Insights, and Martech Platform teams to align architecture with business outcomes.
- Work closely with Enterprise Architects to ensure analytics/AI solutions fit into the broader technology strategy
Performance Optimization & Scalability
- Design for data scale, system performance and latency minimisation across analytical workflows.
- Monitor system health and implement continuous optimisation across AI and analytics pipelines.
Documentation & Knowledge Sharing - Produce and maintain architectural documentation, data flows, AI models architecture, and compliance controls.
- Share best practices across teams and coach domain architects in AI and analytics technologies.
Qualifications and Experience Required
- 10+ years of experience in IT, data, or software engineering, including 5+ years in analytics and AI architecture roles.
- Strong background in consumer and marketing analytics technologies, data platforms, and experience with experimentation platforms.
- Hands-on experience with AI/ML, including GenAI architecture, LLMs, prompt engineering frameworks, and MLOps practices.
- Proven leadership in designing and scaling data-driven solutions across global, matrixed organisations.
- Strong stakeholder engagement skills, with the ability to translate complex technical concepts to non-technical audiences.
Barriers to Success in Role
Success is seen as:
- A scalable, secure, and performant analytics and AI architecture aligned with strategic goals.
- Increased marketing agility and decision making through robust insights and predictive intelligence.
- Consistent implementation of responsible AI and GenAI practices across domains.
- Consolidation of analytics tools and reduction of technical debt.
- Effective partnerships with Product, D&T, Data Science, and Insights functions to unlock business value from data.
