Chubb Life Global is seeking a visionary and technically accomplished leader to serve as the Head of Data Engineering & Architecture. This critical role will be responsible for designing, governing, and executing enterprise-wide data engineering and architecture strategies that enable robust, scalable, and reliable data solutions across 8 Asian markets. The successful candidate will lead the design authority, set and enforce data engineering and solution architecture patterns, and develop a robust enterprise data model to maximize business value for the insurance business. Mastery of the Azure tech stack, Databricks, and Snowflake is essential, as is a strong understanding of the data management lifecycle.

Data Engineering & Solution Architecture

Lead the design, development, and implementation of scalable, secure, and high-performance data engineering solutions using Azure, Databricks, and Snowflake.Establish and chair the design authority, setting standards and best practices for data engineering and solution architecture across the enterprise.Oversee the end-to-end solution architecture for all data programs, ensuring alignment with business objectives and technical requirements.Develop and maintain a robust enterprise data model that supports current and future business needs, ensuring data consistency, quality, and interoperability.Drive the adoption of modern data engineering patterns, including data pipelines, ETL/ELT processes, data lakes, and real-time streaming architectures.

Enterprise Data Model & Architecture

Design and govern the enterprise data model, ensuring it is extensible, reusable, and aligned with industry standards (e.g., ACORD for insurance).Collaborate with business and technical stakeholders to translate business requirements into scalable data architecture solutions.Ensure data models and architecture support advanced analytics, business intelligence, and AI/ML initiatives.

Data Management Lifecycle & Governance

Ensure all data engineering and architecture initiatives adhere to best practices in data management, including data governance, quality, security, and lifecycle management.Work closely with data governance and data management teams to ensure data assets are catalogued, accessible, and reliable.Advocate for continuous improvement in data engineering processes, tools, and standards.

Execution & Delivery

Oversee the execution of complex data engineering projects, ensuring timely delivery, quality, and alignment with business impact goals.Provide technical leadership and mentorship to data engineering teams, fostering a culture of innovation, collaboration, and excellence.Monitor and optimize the performance, scalability, and reliability of data solutions.

Business Impact & Stakeholder Engagement

Partner with business leaders to identify opportunities for leveraging data engineering and architecture to drive business value, operational efficiency, and competitive advantage.Communicate complex technical concepts and architectural decisions to non-technical stakeholders, ensuring alignment and buy-in.Track and report on key performance indicators (KPIs) to demonstrate the business impact of data engineering initiatives.

 

Skills & Qualifications

15+ years of experience in data engineering, solution architecture, or related fields, with a proven track record of delivering complex data programs in large, multinational organizations.Deep expertise in the Azure tech stack, Databricks, and Snowflake, with hands-on experience designing and implementing enterprise-scale data solutions.Strong knowledge of the data management lifecycle, including governance, quality, integration, and security.Demonstrated experience in developing and governing enterprise data models, preferably in the insurance or financial services industry.Exceptional leadership, stakeholder management, and communication skills, with the ability to influence and align diverse teams.Strong analytical and problem-solving skills, with a focus on delivering measurable business impact.Experience with modern data engineering tools, frameworks, and methodologies (e.g., data lakes, ETL/ELT, real-time streaming, CI/CD for data pipelines).Familiarity with industry data standards such as ACORD is a plus.Advanced degree in Computer Science, Engineering, Information Systems, or a related field is preferred.