Data & AI Delivery Leader

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Where is the work:

Monday to Thursday, work onsite with your colleagues. Fridays, choose your work location, balancing what your work requires.

 

 

Role Summary

We are seeking a Data & AI Delivery Leader to provide architecture leadership and delivery direction for strategic enterprise Data and AI initiatives. This role combines the technical depth of a solution architect with the execution ownership of a delivery leader, with equal accountability for enterprise data foundations, analytics architecture, and scalable AI solution delivery, while providing hands-on architecture leadership in key solution areas.

The person will shape end-to-end solution direction, align work to enterprise standards, and guide delivery across cross-functional teams to ensure scalable, secure, high-quality outcomes. This is a leadership role for someone who can translate business needs into practical architecture and execution plans while providing continuity across initiatives spanning data platforms, analytics, semantic layers, AI/ML enablement, generative AI, knowledge retrieval, and intelligent automation. The role is intentionally balanced to lead both trusted data capabilities and AI-enabled business outcomes, while also building long-term team capability through mentoring, coaching, and practical knowledge transfer.

Key Responsibilities

1. Solution Architecture & Technical Leadership

• Own end-to-end architecture for Data and AI solutions, providing hands-on architecture leadership aligned with enterprise strategy, business goals, and platform standards.
• Define scalable, secure, and reusable architecture patterns across data ingestion, transformation, modelling, semantic layers, analytics consumption, AI/ML enablement, and generative AI solution components.
• Translate business capability needs into architecture blueprints, technical decisions, and implementation direction.
• Establish and maintain reference architectures, design guardrails, and reusable solution patterns.
• Ensure solutions are designed for performance, maintainability, interoperability, supportability, and long-term scale.

2. Delivery Leadership & Execution Oversight

• Serve as the delivery leader for solution execution, partnering across product, engineering, governance, security, and business teams from concept through implementation while maintaining reasonable direct involvement in solution shaping and critical execution decisions.
• Shape new initiatives by defining scope, delivery approach, sequencing, dependencies, risks, operating assumptions, and high-level estimates.
• Drive architecture and delivery decisions across multiple concurrent efforts, balancing speed, quality, risk, sustainability, and value realization.
• Help structure work across core and flex capacity, aligning the right skills and teams to the highest-value priorities.
• Support release planning, solution reviews, issue resolution, change management, and adoption planning to ensure successful implementation while staying close enough to help unblock critical issues when needed.

3. Data Architecture, Analytics & AI Solution Design

• Define architecture patterns across cloud-native data platforms, including data lake, warehouse, semantic, and consumption layers.
• Guide conceptual, logical, physical, dimensional, and semantic modelling across enterprise data domains.
• Ensure data structures, metrics, and semantic models are reusable, governed, and aligned to business definitions.
• Partner with data engineers, AI engineers, BI developers, platform teams, and business stakeholders to design scalable data products and AI-enabled solutions.
• Ensure governed data foundations support reporting, self-service analytics, operational insight, AI/ML workloads, and future extensibility, with active hands-on participation in key data and analytics decisions.

4. Enterprise AI Solution Leadership

• Lead design and delivery of enterprise AI solutions, including generative AI, retrieval-augmented generation, enterprise AI assistants, knowledge retrieval, intelligent workflow automation, and predictive analytics use cases.
• Define reusable AI architecture patterns covering prompt orchestration, model integration, retrieval design, vector-based knowledge access, tool integration, and human-in-the-loop controls.
• Guide model and solution design decisions based on business value, feasibility, data readiness, risk, and enterprise supportability.
• Ensure AI-enabled solutions are grounded in trusted enterprise data and designed for measurable business outcomes.
• Help establish reusable AI design patterns, delivery approaches, and guardrails that accelerate future implementations, while staying closely engaged in critical AI architecture decisions and implementation trade-offs.

5. AI Delivery, Evaluation & Operational Readiness

• Define and promote practical delivery patterns for AI solutions from experimentation through pilot, production rollout, monitoring, and continuous improvement.
• Establish evaluation approaches for AI solution quality, groundedness, usefulness, reliability, and user adoption.
• Partner with platform and engineering teams on AI operational readiness, including observability, feedback loops, versioning, change control, and cost-performance optimization, with active involvement in key production-readiness decisions.
• Support scalable adoption of AI capabilities by creating playbooks, reusable assets, implementation patterns, and transition approaches for long-term support.

6. Governance, Responsible AI & Enterprise Standards

• Ensure enterprise standards, best practices, and governance policies are applied consistently across Data and AI solutions.
• Define and promote standards for data quality, lineage, documentation, traceability, security, privacy, operational readiness, and supportability.
• Ensure AI solutions are designed with responsible AI principles, including grounding, transparency, appropriate human oversight, and auditability.
• Partner with governance leaders, stewards, product teams, and security stakeholders to align architecture with business definitions, metadata expectations, access controls, and policy requirements.
• Provide architecture oversight for compliance, resiliency, cross-domain consistency, and secure enterprise AI adoption.

7. Cross-Functional Leadership & Stakeholder Engagement

• Act as a key interface between business stakeholders, product leaders, governance leaders, engineering teams, and enterprise technology leadership.
• Communicate complex architecture options, AI solution trade-offs, and delivery implications clearly to both technical and non-technical audiences.
• Facilitate architecture reviews, solution workshops, prioritization discussions, and design decisions.
• Build strong domain and business context over time to improve decision-making and delivery quality.
• Mentor team members and provide leadership across architects, engineers, analysts, AI practitioners, and delivery partners, while building long-term Data and AI capability through coaching, reusable playbooks, and practical knowledge transfer.

Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, Software Engineering, Artificial Intelligence, or a related technical field.

Required Qualifications

• 10+ years of experience in solution architecture, data architecture, enterprise data platforms, analytics delivery, AI solution design, or related technology leadership roles.
• Strong experience leading end-to-end solution design and delivery for complex data, analytics, and AI initiatives in enterprise environments.
• Deep understanding of cloud data platforms, preferably GCP / BigQuery or equivalent enterprise cloud ecosystems.
• Strong expertise in data architecture, data modelling, semantic design, analytical solution patterns, and governed data foundations.
• Practical experience designing or leading enterprise AI solutions, including generative AI, knowledge retrieval, AI assistants, or ML-enabled decision-support capabilities.
• Demonstrated ability to balance enterprise data platform thinking with AI solution delivery and business adoption.
• Experience guiding cross-functional teams across architecture, engineering, analytics, governance, security, and business stakeholders.
• Proven ability to connect strategy, architecture, and delivery execution in a domain-oriented operating model.
• Strong understanding of data governance, metadata, lineage, quality, security, responsible AI, and enterprise standards.
• Experience shaping delivery scope, sequencing, dependencies, implementation approaches, and operating models for multi-team initiatives.
• Strong communication, leadership, stakeholder management, and decision-making skills.
• Ability to operate effectively in ambiguity and lead through change in a transforming enterprise environment.

Preferred Qualifications

• Experience supporting enterprise AI assistants, retrieval-augmented generation, knowledge retrieval, or similar Data and AI solution patterns.
• Experience defining AI governance guardrails, model risk controls, or responsible AI practices in an enterprise setting.
• Familiarity with metadata, data catalog, governance, vector search, or knowledge platform capabilities.
• Experience with Git-based delivery practices, DevOps, CI/CD, and modern engineering standards for data and AI platforms.
• Exposure to domain-oriented data product principles and federated delivery models.
• Experience helping scale AI adoption through reusable patterns, playbooks, delivery frameworks, or platform enablement.

Key Success Profile

The ideal candidate is a hands-on enterprise architecture and delivery leader who can bridge business priorities and technical execution, provide strong delivery accountability, and help build scalable, governed, enterprise-grade Data and AI capabilities. They should be equally comfortable shaping data foundations, leading cross-functional delivery, staying actively engaged in critical solution decisions, and accelerating pragmatic enterprise AI adoption while building long-term team capability through mentoring, coaching, and practical knowledge transfer.

We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.