Head of AI Data Engineering

Responsibilities

Role Overview

A senior technology leader is sought to define and advance the data foundations required to support enterprise-scale AI adoption. The role combines strategic ownership with significant technical involvement, covering the architecture, engineering, governance and ongoing development of data capabilities used by AI applications, intelligent automation and advanced analytical solutions.

The successful candidate will shape a scalable ecosystem of reusable data assets, knowledge structures, retrieval capabilities and supporting platforms. The role requires the ability to translate emerging AI requirements into robust engineering patterns and enterprise capabilities that can be applied across diverse use cases.

This is a senior leadership position requiring both organisational leadership and strong technical judgement. The successful candidate will guide multidisciplinary engineering capabilities while remaining closely involved in architecture, technology choices, engineering standards and the delivery of production-grade platforms.

Scope of the Role

Enterprise AI Data Strategy

• Define and evolve an enterprise approach to preparing, structuring and delivering data for AI-enabled applications.
• Establish architectural principles, reusable engineering patterns and standards for AI-focused data capabilities.
• Shape the evolution of data, knowledge and retrieval architecture in line with broader technology and business priorities.
• Determine how common capabilities can be developed for reuse across different applications and functional areas.

Technical & Engineering Leadership

• Provide technical direction across data engineering, knowledge engineering, platform capabilities and related AI data disciplines.
• Lead multidisciplinary technical teams and develop the capabilities required for modern AI data engineering.
• Maintain close involvement in significant architectural and engineering decisions.
• Promote strong engineering practices, technical accountability, knowledge sharing and continuous capability development.

AI Data Products & Knowledge Foundations

• Establish reusable data products and services designed to support multiple AI and analytical applications.
• Develop structured representations of enterprise knowledge, including semantic models, taxonomies and interconnected knowledge assets where appropriate.
• Improve the discoverability, usability, quality, scalability and lifecycle management of data and knowledge assets.
• Ensure AI-focused data capabilities are designed with appropriate security, governance and operational controls.

Context, Retrieval & Knowledge Access

• Develop enterprise capabilities for supplying AI systems with relevant, reliable and appropriately structured context.
• Define engineering approaches for information retrieval, semantic search, vector-based retrieval and retrieval-augmented AI applications.
• Establish reusable patterns for contextual information, knowledge retrieval and persistent information used by intelligent systems.
• Introduce appropriate approaches to evaluation and optimisation of retrieval and context quality.

Production Engineering & Platform Reliability

• Review architectures, pipelines, data products and supporting services to ensure they are suitable for production environments.
• Drive improvements in data quality, availability, freshness, performance, scalability and operational efficiency.
• Establish practices for monitoring, observability, resilience, maintainability and lifecycle management.
• Investigate complex technical issues and lead structured root-cause analysis and remediation.
• Continuously improve the underlying data capabilities supporting deployed AI solutions.

Governance, Security & Trust

• Establish standards covering data quality, metadata, lineage, access, privacy, security and appropriate use of AI-related data.
• Build governance mechanisms that support responsible deployment without unnecessarily restricting innovation.
• Embed appropriate controls throughout the design, development and operation of AI data assets.
• Work with relevant control functions to address organisational, security and regulatory requirements.

Platform & Capability Development

• Translate strategic priorities into scalable data products, services and platform capabilities.
• Enable a broad range of AI applications, intelligent workflows, automation and analytical use cases through reusable foundations.
• Assess emerging technologies and determine where they can provide meaningful improvements to enterprise capabilities.
• Lead decisions involving technology selection, architectural direction, internal development and external solutions.
• Prioritise technical investments according to business value, scalability, feasibility and long-term sustainability.

Enterprise Delivery & Stakeholder Leadership

• Work across technology, data, AI, security, governance and business functions to coordinate complex delivery programmes.
• Establish priorities, manage technical dependencies and resolve cross-functional delivery challenges.
• Communicate architectural decisions, technology considerations, investment requirements and trade-offs clearly to senior stakeholders.
• Connect long-term platform strategy with tangible delivery outcomes.

Requirements

What You Bring

Essential Experience

• Extensive senior-level experience leading data engineering, data platforms, AI data infrastructure or closely related technology capabilities.
• Proven experience designing and delivering enterprise-scale data platforms or reusable data services.
• Strong understanding of modern approaches to preparing and delivering data for AI applications.
• Demonstrated experience leading multidisciplinary technical teams and building engineering capability at scale.
• Strong architectural judgement, with the ability to make and communicate complex technology decisions.
• Experience taking data and AI capabilities from conceptual design through production operation and continuous improvement.
• Strong understanding of data governance, security, quality, metadata, lineage and operational controls.
• Demonstrated ability to translate business objectives into scalable technical capabilities and measurable outcomes.

Technical Expertise

Strong knowledge across several of the following areas is expected:

• Modern data engineering and distributed data platforms
• AI-oriented data architecture and data products
• Knowledge representation and semantic modelling
• Knowledge graphs and interconnected information structures
• Search, retrieval and semantic information access
• Vector-based retrieval and embeddings
• Retrieval-augmented AI architectures
• Context management and information orchestration
• Data quality, metadata and lineage
• Platform reliability, observability and performance engineering
• AI data governance, security and access management
• Evaluation and optimisation of AI data and retrieval capabilities

The successful candidate should be able to understand the interaction between these disciplines rather than treating them as isolated technical components.

Leadership Profile

The role requires a leader who can operate comfortably at both strategic and technical levels.

You will be expected to:

• Set direction while remaining sufficiently hands-on to challenge architectural and engineering decisions.
• Build high-performing technical organisations and develop senior engineering talent.
• Balance speed of innovation with reliability, governance, security and long-term maintainability.
• Make pragmatic decisions where technology, cost, risk and business value must be considered together.
• Influence senior stakeholders across functions without relying solely on formal authority.
• Establish repeatable engineering practices rather than solving AI data requirements as isolated projects.
• Navigate evolving AI technologies while maintaining a disciplined enterprise architecture.

Expected Outcomes

Success in the role will be reflected in the establishment of a scalable and reusable AI data foundation that:

• Provides reliable, accessible and well-governed data and knowledge for AI applications.
• Reduces duplication through reusable enterprise capabilities and engineering patterns.
• Improves the quality and relevance of information supplied to AI and intelligent systems.
• Supports production workloads with appropriate reliability, security, observability and operational discipline.
• Enables teams across the organisation to develop and deploy AI capabilities more efficiently.
• Creates a sustainable technical foundation capable of evolving alongside rapidly changing AI technologies.

Application:

Apply to this job posting, and send your CV with the job title as the subject line to: [Confidential Information] & https://www.linkedin.com/in/treasa-wong/