Chubb Life Global Office Head of Data Engineering & Architecture

Chubb Life Global is seeking a visionary and technically accomplished leader to serve as theHead 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.