Senior Manager, Data Platform Engineering

Prudential’s purpose is to be partners for every life and protectors for every future. Our purpose encourages everything we do by creating a culture in which diversity is celebrated and inclusion assured, for our people, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and we support our people’s career ambitions. We pledge to make Prudential a place where you can Connect, Grow, and Succeed.

Senior technical and people leader responsible for driving the end-to-end delivery of the Data Lake platform, Enterprise Data Model, data ingestion, and curated data services that power data & analytics, AI, regulatory, and business reporting needs. The role leads a multi-disciplinary team of data engineers and contingent specialists, while partnering closely with Data Governance, Enterprise Architecture, AI Engineering, Business Units, Group Technology, and external vendors to deliver trusted, governed, and scalable data assets.

Key Responsibilities

one. Team Leadership

• Lead, coach, and develop a team of data engineers, platform engineers, and contingent workers across onshore and offshore models, including direct line management of permanent staff and contractors.

• Provide day-to-day technical guidance, code/design reviews, and architectural direction to the team on Azure Databricks, ADF, Cosmos DB, MongoDB, SQL, Python/Scala, Qlik Replicate, Fivetran, Enterprise Kafka, DataStage, Informatica and ingestion frameworks.

• Drive performance management, career development planning, and succession planning for the team.

• Build a high-performing culture of accountability, quality, and continuous learning; identify upskilling/cross-training needs and nominate talent for recognition.

• Support hiring, interviewing, and onboarding of new team members and vendor resources.

two. Data Platform Engineering & Architecture Delivery

• Own the roadmap, design, build, and operations of the Data Lake platform, including raw, structured, EDM, and curated/serving layers on Azure Databricks and Azure Data Stack.

• Drive the data uplift programme and convergence/decommissioning initiatives (e.g., convergence, legacy data store remediation).

• Lead the design of scalable, reusable ingestion patterns (batch and near real-time) from core systems such as policy admin system, claims, marketing, and partner systems.

• Govern adoption of the group data model alignment (including SCD2 patterns, incremental processing, timestamp metadata) in line with Group/Regional standards.

• Champion engineering best practices: CI/CD, modular pipeline design, parameterised/config-driven frameworks, monitoring, and observability.