Manager/Senior Manager - Data Management

Capgemini Invent

At Capgemini Invent, we believe difference drives change. As inventive transformation consultants, we blend our strategic, creative and scientific capabilities, collaborating closely with clients to deliver cutting-edge solutions. Join us to drive transformation tailored to our client's challenges of today and tomorrow. Informed and validated by science and data. Superpowered by creativity and design. All underpinned by technology created with purpose. Join our Enterprise Data & Analytics (EDA) Financial Services team and be part of the future of AI consulting. We are redefining how organisations adopt and scale AI, moving beyond experimentation to embedding agentic systems, human-AI workflows and data products into every aspect of how they operate, compete, and create value. We are looking for experienced Data & AI leaders who combine AI technical expertise and commercial acumen to shape enterprise-wide transformation. You will lead client engagements from strategy through to execution that combine modern data platforms, semantic architectures and agentic AI solutions to deliver measurable business value, whilst contributing to the growth of our EDA Financial Services practice.

Your Role

This role is for a programme leader with genuine technical depth

• not a solution architect, and not a pure strategist. You will own complex, multi-workstream data and AI programmes for major Financial Services clients: the client relationship, the commercials, the delivery and the team. You will need enough hands-on background to challenge an architecture decision credibly, and enough consulting judgement to hold a boardroom. You will not be building the architecture yourself. You will work across retail and commercial banking, insurance and capital markets, on problems where the value is clear and the constraints are real
• credit decisioning, underwriting, claims, KYC and AML operations, complaints handling and trade surveillance, alongside the data foundations underneath them. You will be expected to operate fluently within the regulatory environment that shapes AI delivery in Financial Services. We Make AI Useful. You will own the commercial and value agenda
• making sure the AI we build is worth building. • Acting as a trusted advisor to executive and C-suite stakeholders, shaping AI strategy, investment cases and roadmaps, and being the person the client escalates to when the programme is under pressure • Owning the business case end to end: defining the value, agreeing the measurement approach with the client’s finance and risk functions, and being accountable for the benefits actually landing • Making the difficult trade-off calls
• build versus buy, platform choice, delivery sequencing, and where technical debt is acceptable and where it is not We Make AI Usable. You will lead the human-centred design of AI solutions around the needs of users and the way organisations operate. • Translating business and regulatory problems into AI-enabled products, agentic workflows and intelligent decision platforms, with defined user needs, business outcomes and success metrics • Designing operating models that integrate people, AI agents and business processes
• including role redesign, human-in-the-loop controls, and the change strategy that makes adoption stick • Partnering with business, product and engineering leaders to embed AI into customer journeys, colleague experience and operational decision-making at enterprise scale We Make AI Reliable. You will set the technical direction and hold the delivery to account, without designing it yourself. • Directing the design of AI-ready data foundations, platforms and semantic layers that provide trusted business context and reliable access to enterprise information, and holding your architects to account on those decisions • Assuring agentic architectures
• interrogating how agents interact with enterprise systems and people, where they fail, how failure is detected, and what the cost curve looks like at scale • Establishing the governance, security, responsible AI and operational practices required to run AI in production under regulatory scrutiny As part of your role, you will be expected to contribute to the business and your own personal growth, through activities that form part of the following categories: • Business Development
• Leading proposals, RFPs, bids, proposition development, client pitches and Thought leadership that positions the organization at the forefront of modern Data & AI consulting. • Internal contribution
• Supporting practice development through offering development, recruitment, campaign development, internal think-tanks, whitepapers, operational excellence and capability building across Data, AI and Agentic AI. • Learning & Development
• Continuously developing your own expertise in emerging data and AI technologies and consulting leadership through training, certifications and applied innovation, while fostering a culture of continuous learning across the practice.

Your Profile

We are looking for a T-shaped leader: broad enough to run an enterprise programme end to end, and deep enough in data and AI to be a genuine peer to the engineers and architects you lead. What matters most is your ability to connect business problems, regulatory constraints and technology into outcomes clients can actually operate. The essential criteria below are what we screen against, and we expect candidates to meet all of them. The desirable experience that follows is genuinely a bonus

• strong candidates will have several, and nobody has all of them. We value diverse backgrounds, perspectives and experiences, and unless stated otherwise these criteria can be evidenced in any sector. Essential criteria • Programme leadership at scale. You have led complex, multi-workstream data or AI programmes with teams, and have been personally accountable for the outcome
• not for a workstream within someone else’s programme. • AI delivered into production, not just piloted. You can describe at least one AI or advanced analytics capability you took from concept into live operational use, including what went wrong along the way and how you dealt with it. • Financial Services experience. Experience delivering for banking, insurance or capital markets clients, with a solid working understanding of how these businesses operate, what regulation means for them and what actually drives value. You can translate business requirements into delivery that produces measurable outcomes. • Technical credibility. Sufficient hands-on background in data engineering, data platforms, machine learning or Generative AI to be a genuine peer to your architects and engineers. You should be able to explain, without notes, how a RAG architecture fails in production and what you would do about it. • Team leadership. Experience leading and coaching consultants, engineers or architects within your programme, and building an inclusive, high-performing team culture people want to stay in. • Consulting and commercial track record. Experience in a consulting or senior client-facing environment advising executive stakeholders and presenting to client governance boards, with a demonstrable record of originating and selling work. Desirable experience • Hands-on delivery experience with one or more major cloud and data platforms
• Databricks, Microsoft Azure, AWS, GCP or Snowflake
• and familiarity with the wider AI ecosystem including OpenAI, Anthropic and Google • Experience designing semantic layers, data products or knowledge platforms that serve both analytics and AI consumers • Experience shaping agentic architectures
• how AI agents interact securely with enterprise systems, data platforms, business processes and human decision-makers • Practical experience of responsible AI in production: model evaluation, line