Location
London, with a hybrid working arrangement requiring 2 days in the office per week.

Hours
Full Time - 35 hours per week

Salary
Negotiable

About the Role
WTW’s Risk & Broking data function is building data products that help colleagues make better, faster, and more consistent decisions. Operating as part of the central data function, the team collaborates with engineering, domain specialists, regional data colleagues, and business stakeholders to create products for Great Britain and the global business. We are seeking a hands-on Senior Data Product Manager to turn ambiguous business problems into viable data-product opportunities. This individual-contributor role requires combining product judgement, analytical problem-solving, and technical prototyping to test ideas quickly, establish value, and lead selected products through development, launch, and ongoing improvement.

The role involves product discovery and problem definition by working with stakeholders to understand decisions, workflows, pain points, and opportunities addressable through data products. You will convert broad challenges into clear problem statements, target users, desired outcomes, hypotheses, and measurable objectives. Assessing product opportunities based on business value, user need, data readiness, technical feasibility, scalability, and portfolio alignment is key.

You will personally build working proofs of concept using tools such as SQL, Python, Databricks, Power BI, AI tooling, and low-code platforms. Exploring and profiling data to understand quality, coverage, and suitability is essential. Prototypes will be used to test concepts with users, expose assumptions, and generate evidence before engineering investment. Collaboration with data engineers, architects, analysts, and platform specialists ensures successful prototypes are industrialised into maintainable, governed, and scalable solutions.

As product owner, you will manage selected data products from discovery through development, launch, adoption, and improvement. Responsibilities include defining vision, outcomes, scope, roadmap, success measures, and priorities. You will translate user and business needs into product and data requirements, make prioritisation decisions, and balance experimentation speed with data quality, governance, security, and maintainability.

Building effective relationships with stakeholders across business, domain, data, and technology teams is crucial. You will facilitate structured product conversations, clarify technical concepts for non-technical audiences, create transparency around assumptions and risks, and help establish consistent data-product practices.

Finally, you will define and monitor product performance measures, use feedback to improve or scale products, ensure clear documentation, and contribute reusable methods and lessons to the wider data-product capability.

Experience
- Proven experience across data, analytics, technology, or digital products with a strong track record of owning and delivering data-enabled products or solutions.
- Demonstrated ability to solve ambiguous business problems and convert them into practical, evidence-based solutions.
- Hands-on experience interrogating data and building working prototypes using several of SQL, Python, Databricks, Power BI, AI tooling, or low-code tools.
- Experience in product discovery, defining user needs, setting outcomes, prioritising work, and collaborating with engineering teams.
- Ability to assess data quality and technical feasibility independently.
- Experience taking products or analytical solutions beyond ideation into sustained use.
- Strong written and verbal communication skills with business, domain, data, and technology stakeholders.
- Ability to work independently while building alignment across a complex organisation.

About you
- Comfortable moving between business problems, data, technology, and product delivery.
- Proactive and hands-on with a strong analytical mindset.
- Collaborative with excellent stakeholder management skills.
- Detail-oriented with a focus on data quality, governance, and security.
- Adaptable to a fast-paced, evolving environment.

Qualifications
Desirable:
- Experience in commercial insurance, insurance broking, specialty insurance, claims, placement, portfolio analytics, or underwriting.
- Familiarity with data mesh, domain-oriented data products, common data models, data contracts, metadata, and data governance.
- Experience industrialising analytical prototypes with data-engineering and architecture teams.
- Experience integrating generative AI or machine learning into business products.
- Knowledge of regulated environments and responsible use of commercially sensitive data.