AbsoluteLabs
Data Product Lead
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Data & AI Product Lead
Data Architecture | AI Strategy | Product Innovation
About the Role
The Data & AI Product Lead owns the strategy, architecture, and adoption of data and artificial intelligence products across the organisation. This role partners with business, engineering, and delivery leaders to identify high-impact AI and data opportunities, build a robust data architecture foundation, and drive measurable business value through responsible, scalable AI adoption.
The Role
Role purpose
As Data & AI Product Lead, you will define the data and AI strategy, own the underlying data architecture, and lead the design, build, and scaling of AI-powered products and agent ecosystems. You will turn business logic into automation, connect delivery and product data into centralised platforms, and ensure AI and data systems are governed, secure, and genuinely adopted across the business.
This is a hands-on product and architecture leadership role, not a purely strategic or advisory position. You will build and own the data architecture and agent ecosystem end-to-end, working closely with engineers, data scientists, consultants, and business teams to deliver tools that are practical, trusted, and used at scale.
Key Responsibilities
AI and data strategy leadership
- Define and drive the Data & AI product strategy in alignment with overall business objectives.
- Identify, evaluate, and prioritise AI, ML, and data use cases across the enterprise.
- Track industry trends and emerging technologies (Generative AI, Agentic AI, ML, automation) to inform roadmap decisions.
- Present data and AI roadmaps and business cases to executive leadership.
Data architecture and platform design
- Define enterprise data architecture, including data models, pipelines, and integration patterns that underpin AI and analytics products.
- Design scalable, secure data platforms spanning batch and streaming pipelines, APIs, and operational systems.
- Establish data quality, lineage, and governance standards across the data estate.
- Partner with engineering and platform teams to select and evolve cloud and AI/ML platforms.
Agent ecosystem and workflow automation
- Design and build a set of AI agents (for example, scoping, estimation, risk) that work together as a single system.
- Define end-to-end workflows across the product lifecycle, ensuring smooth hand-offs between agents and humans and reducing manual steps and rework.
- Convert business logic, scoping rules, estimation models, and governance policies into working, automated tools.
- Continuously improve agents and models based on real delivery and usage data.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Data and feedback loops
- Connect delivery and product data into a centralised data platform.
- Track key metrics such as estimate versus actual and margin versus plan.
- Use data and feedback loops to continuously improve products, agents, and models over time.
Governance and responsible AI
- Establish governance frameworks for responsible, ethical, and secure use of AI and data.
- Build tools that automatically check outputs against standards, flag risks early, and enforce approval rules.
- Own AI and data system governance, including data privacy, model integrity, observability, and cost management.
Team and stakeholder leadership
- Lead cross-functional teams to design, pilot, and scale AI and data-powered products.
- Partner with consultants and business teams to understand real-world challenges, and with delivery centre teams to build and scale solutions globally.
- Build and manage relationships with internal stakeholders and external technology partners and vendors.
- Mentor and grow a team focused on data architecture, innovation, and applied AI delivery.
- Measure and report on ROI and adoption metrics for AI and data initiatives.
Candidate Profile
Essential experience and capabilities
- 15+ years of experience in technology strategy, data architecture, innovation, or AI/ML delivery, including 5+ years in a leadership role.
- Strong understanding of AI/ML concepts, Generative AI, and enterprise data and technology architecture.
- Hands-on experience with Large Language Models, agentic frameworks, MLOps, and traditional AI and ML.
- Proven experience designing enterprise data architectures, including data models, pipelines, APIs, and batch/streaming integration.
- Deep understanding of AI and data governance, including data privacy, model integrity, observability, and cost management.
- Proficient in cloud technologies and enterprise-grade AI/ML and data platforms (for example, AWS SageMaker, Bedrock, Azure ML, Databricks).
- Expertise in LLMs, including fine-tuning, RAG, prompt engineering, multi-agent collaboration, orchestration frameworks, and alignment techniques.
- Demonstrated experience setting strategic technology direction and architecture vision for a large organisation, including target-state roadmaps.
- Proven track record leading cross-functional innovation programmes and driving change management and adoption at scale.
- Excellent stakeholder management, communication, and executive presentation skills.
- Expert-level software engineering experience.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Desirable experience
- Experience with enterprise SaaS ecosystems and cloud platforms.
- Familiarity with AI governance, responsible AI practices, and data privacy regulations.
- Background in consulting, delivery management, or product innovation.
- Experience partnering with hyperscalers and technology vendors on build-versus-buy evaluations.
What Success Looks Like
Success in this role means building a thriving data and AI culture across the organisation, scaling AI and data capabilities to deliver measurable business impact, and growing a best-in-class portfolio of reusable data and AI solution components. It also means expanding the organisation's AI, ML, and data architecture capability and talent over time.
What Kind of Person Succeeds Here
- Builds, not just designs.
- Thinks in systems and workflows, not isolated tools.
- Comfortable working with both engineers and consultants or business teams.
- Focused on solving real problems, not building “cool demos”.
- Can simplify complexity and make tools easy to use.
What's on Offer
- Competitive compensation and benefits.
- A high-visibility leadership role shaping enterprise-wide data and AI strategy.
- Ownership of the data architecture and agent ecosystem end-to-end, from strategy through to build and scale.
- Opportunity to grow and mentor a team focused on data architecture and applied AI.
- Career growth within a fast-moving, innovation-focused organisation.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location