Data Product Owner – Insurance – Hybrid

Data Product Owner - Insurance - Hybrid focuses on managing, refine, and prioritise the product backlog to align with the overarching data strategy and transformation roadmap.

What the role involves

• Managing, refine, and prioritise the product backlog to align with the overarching data strategy and transformation roadmap.
• Data Governance & Quality: Ensure that all data transformation activities adhere to internal data governance frameworks, security policies, and London Market regulatory requirement.
• Cross-functional Delivery: Work closely with business data analysts, data architects, engineers, and data scientists to ensure that the solutions being built are feasible, scalable.
• Driving the adoption of new data platforms or models by communicating value to end-users and overseeing UAT (User Acceptance Testing) and business readiness activities.
• Ability to read/interpret data models and SQL queries to drive product decisions.

Skills and requirements

• Prior experience within the London Market (Insurance/Reinsurance) or highly regulated financial services environments.
• Product Experience: Proven track record as a Product Owner or Business Analyst within large-scale data transformation, migration, or analytics programmes.
• Extensive experience working in Agile/Scrum delivery environments (ADO, Confluence).
• Experience with specific London Market data standards (e.g., Exposure to AI/ML product development in a financial context.

Confirmed role details

• Week Onsite We are seeking an experienced Product Owner with a strong Business Analyst background and proven experience within the London Market Reinsurance domain.

Additional role context

• The individual will be responsible for the end-to-end delivery of data-centric initiatives within a Tier-1 London Market insurer.
• Performance Metrics: Define and track KPIs for data products (e.g., data quality scores, reduction in manual processing time, platform adoption rates).
• Strong understanding of modern data stacks (e.g., Cloud data platforms like Databricks/Azure, ETL/ELT pipelines, BI tools).