Head of AI Data Engineering

Role Overview

This senior technology leadership role is focused on establishing and advancing the enterprise data foundations necessary for large-scale AI adoption. It combines strategic leadership responsibilities with hands-on technical involvement, encompassing architecture, engineering, governance, and ongoing enhancement of data capabilities for AI applications, intelligent automation, and advanced analytics.

The selected candidate will be instrumental in crafting a scalable ecosystem comprising reusable data assets, knowledge frameworks, retrieval systems, and supporting platforms. Translating evolving AI requirements into robust engineering patterns and enterprise-ready capabilities applicable across diverse business contexts is a key expectation.

This senior leadership position demands a blend of organizational stewardship and deep technical judgement, overseeing multidisciplinary engineering teams while remaining directly engaged with architecture, technology decisions, engineering standards, and delivering production-grade platforms.

Key Responsibilities

• Define and continuously refine an enterprise-wide strategy for preparing, structuring, and delivering data tailored to AI-enabled applications.
• Set architectural principles, reusable engineering patterns, and standards specifically targeting AI data capabilities.
• Guide the progression of data, knowledge, and retrieval architectures aligning with broader technological and business goals.
• Identify and develop common reusable capabilities applicable across a variety of applications and business functions.
• Offer technical leadership covering data engineering, knowledge engineering, platform capacities, and allied AI data domains.
• Lead and nurture multidisciplinary technical teams, fostering capabilities essential for contemporary AI data engineering.
• Remain closely engaged in significant architecture and engineering decisions while championing rigorous engineering best practices, accountability, knowledge exchange, and ongoing skill development.
• Create reusable AI data products and services that support a range of AI and analytical applications.
• Develop structured enterprise knowledge representations such as semantic models, taxonomies, and interconnected knowledge assets.
• Enhance discoverability, usability, quality, scalability, and lifecycle management of data and knowledge resources.
• Ensure AI data capabilities incorporate necessary security, governance, and operational controls.
• Build enterprise solutions supplying AI systems with relevant, reliable, and well-structured context.
• Design engineering approaches for advanced information retrieval techniques, including semantic search, vector-based retrieval, and retrieval-augmented AI.
• Establish reusable engineering patterns for contextual information, knowledge access, and persistence in intelligent systems.
• Implement evaluation and optimization strategies for retrieval and contextual information quality.
• Review and ensure production readiness of architectures, data pipelines, products, and support services.
• Drive enhancements in data quality, availability, freshness, performance, scalability, and operational efficiency.
• Set practices to assure monitoring, observability, resilience, maintainability, and lifecycle management.
• Lead thorough investigations into complex technical issues and organize root-cause analyses with remediation efforts.
• Continuously evolve the underpinning data capabilities supporting existing AI implementations.
• Define and enforce standards for data quality, metadata, lineage, access, privacy, security, and ethical AI data use.
• Construct governance frameworks that enable responsible AI deployment without hindering innovation.
• Incorporate controls into the design, development, and operation phases of AI data assets.
• Collaborate with control functions to meet organizational, security, and regulatory mandates.
• Translate strategic objectives into scalable data products, services, and platform capabilities.
• Support a wide spectrum of AI applications, intelligent workflows, automation, and analytics through reusable foundations.
• Evaluate emerging technologies to identify opportunities for enhancing enterprise data capabilities.
• Lead technology selection, architectural direction, decisions between internal development and external solutions.
• Prioritize technical investments by considering business value, scalability, feasibility, and long-term sustainability.
• Coordinate cross-functional delivery efforts involving technology, data, AI, security, governance, and business units.
• Set priorities, manage technical dependencies, and address delivery challenges.
• Communicate architectural choices, technology trade-offs, and investment needs effectively to senior stakeholders.
• Link long-term platform strategies with practical delivery results.

Required Qualifications and Experience

• Extensive senior leadership experience in data engineering, platform development, AI data infrastructure or closely related fields.
• Proven track record designing and deploying enterprise-scale data platforms or reusable data services.
• Deep understanding of modern methodologies for preparing and delivering data tailored for AI.
• Experience leading multidisciplinary technical teams and scaling engineering capabilities.
• Strong architectural insight capable of making and communicating complex decisions.
• History of progressing data and AI capabilities from concept to production and continuous enhancement.
• Comprehensive knowledge of data governance, security, quality assurance, metadata, lineage, and operational controls.
• Ability to translate business goals into scalable technical solutions with measurable impact.

Technical Expertise Areas

• Modern data engineering and distributed data platform technologies.
• AI-centric data architectures and data product development.
• Knowledge representation systems and semantic modeling techniques.
• Knowledge graphs and interconnected information frameworks.
• Search technologies including semantic and vector-based retrieval methods.
• Retrieval-augmented AI system architectures and context management.
• Data quality management, metadata handling, and lineage tracking.
• Designing for platform reliability, observability, and performance engineering.
• AI data governance, security frameworks, and access management.
• Evaluation and optimization of AI data retrieval and contextual systems.

Leadership Characteristics

• Ability to set strategic direction while staying technically engaged to critically assess architecture and engineering choices.
• Skill in building high-performing technical teams and mentoring senior engineers.
• Balance rapid innovation with governance, security, and sustainable maintainability.
• Pragmatic decision-making that weighs technical, business, cost, and risk factors.
• Influence senior leaders without depending solely on formal authority lines.
• Establish repeatable engineering processes rather than ad-hoc AI data solutions.
• Navigate evolving AI technology landscapes while maintaining a disciplined enterprise architecture.

Expected Success Metrics

• Creation of a scalable, reusable AI data foundation delivering reliable, accessible, and governed data and knowledge.
• Reduction of duplication via enterprise-wide reusable capabilities and engineering patterns.
• Enhancement of the quality and relevance of AI system information sources.
• Support for production workloads with strong reliability, security, observability, and operational discipline.
• Improved efficiency for teams developing and deploying AI solutions across the organization.
• Establishment of a sustainable technical platform that adapts to fast-changing AI technologies.