Us
SeniorDirector,AI-ReadyDataPreparation
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“Senior Director, AI-Ready Data Preparation at Us. Skills: AI-ready data preparation, Data product development, DataOps, Cloud data platforms, Data engineering leadership, AI/ML, Analytics. Lead the strategy and execution for AI-ready data preparation across high priority AI use cases. Build, own, and operate high value enterprise data products for AI/ML and analytics”
What You'll Achieve.
Accelerate results across the whole value chain; Measure of success is adoption and scale, not just innovation; Accelerate time to insight for AI/ML and analytics through compliant, reliable, and efficient pipelines; Driving increasing automation and self service for data consumers; Drive trust and adoption; Scale successful patterns
Industry & Context.
Turn sophisticated, cross-functional problems into reusable, enterprise-wide capabilities; Solve sophisticated scientific and technical challenges
What They're Looking For.
Must Have
Extensive data engineering leadership with a track record delivering production grade data products and platforms supporting AI/ML and analytics, Data product and DataOps expertise: Hands on proficiency with data product contracts, lineage, quality SLAs, CI/CD for data, automated testing, and metadata driven pipelines that enable self service, Integration, orchestration, and storage: Deep experience with batch/streaming integration, workflow orchestration, and scalable storage/processing on modern cloud data platforms, including performance and cost optimisation, Security and obfuscation: Practical application of enterprise security controls, secure data transfer, and approved obfuscation techniques in regulated environments, Architecture and design patterns: command of applying enterprise data/application design patterns and microservices/event driven approaches using sanctioned tools and reference implementations, Able to move at pace and lead and inspire technical team, whilst working within a rapid evolving, often ambiguous business context, Innovates, experiments and brings external perspectives, with a track record of applying innovative technologies and approaches to increase speed, quality and compliance if data, Partner and vendor management: Ability to influence senior collaborators, manage partner ecosystems, and build high performing clear communication of value, outcomes, and adoption metrics
Nice to Have
Experience leading large-scale AI/ML or analytics transformation programmes in sophisticated global organisations, Background in building or operating shared enterprise data platforms or internal data marketplaces, Familiarity with regulated industries such as life sciences, healthcare, or financial services, Proven track record to translate strategic business priorities into actionable roadmaps for AI-ready data capabilities, Track record of nurturing engineering excellence through coaching, standards, and communities of practice
What You'll Do.
Lead the strategy and execution for AI-ready data preparation across high priority AI use cases
and operate high value enterprise data products for AI/ML and analytics
Implement DataOps practices and AI-driven automation that streamline ingestion
Design and run robust end-to-end data pipelines (ingest/store → prepare → provision)
Apply enterprise security controls and approved obfuscation patterns
Build and maintain exemplar implementations and reusable components
Lead and grow high performing engineering teams
Manage suppliers effectively
high quality delivery
Evaluate and pilot emerging capabilities within enterprise guardrails
Make clear buy versus configure recommendations
Generate evidence of value and adoption to scale successful patterns
How You'll Work.
Team & Collaboration
Actively harness existing capabilities; Celebrate and promote reuse; Export breakthrough ideas across geographies and functions; Thrive in high-collaboration environments; Partner and vendor management; Ability to influence senior collaborators; Teams collaborate across subject areas
Communication Scope
Clear communication of value, outcomes, and adoption metrics
Process & Methodology
Ensure on time, high quality delivery, Translate strategic business priorities into actionable roadmaps
Full Job Description
**Senior Director, AI-Ready Data Preparation** We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being outstanding connectors: you'll actively harness existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn sophisticated, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real. **Introduction to role** This role leads the enterprise capability that prepares, processes, and provisions AI-ready data at scale, using approved standards, tooling, and guidelines. The team builds and operates next generation data preparation, integration, orchestration, transfer, and obfuscation capabilities, driving increasing automation and self service for data consumers. The Senior Director also delivers and owns priority enterprise data products, demonstrating how to apply enterprise patterns end to end to accelerate time to insight for AI/ML and analytics through compliant, reliable, and efficient pipelines. Ready to develop how AI-ready data is composed and consumed across a global organisation? **Accountabilities** Lead the strategy and execution for AI-ready data preparation across high priority AI use cases, shifting from custom solutions to reusable, self-serviceable data products with growing automation. Build, own, and operate high value enterprise data products for AI/ML and analytics, applying enterprise schemas, lineage models, SLAs/SLOs, data contracts, and quality rules that drive trust and adoption. Implement DataOps practices and AI-driven automation that strea
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