Join us on our digital-first journey and join the magic that is happening right now at Chubb. Our teams are innovating, iterating, and solving customer problems at a rapid pace. We are building upon our strength of delivering superior coverage and customer experience by leading the modernization of what insurance does and how we deliver it in a digital way. This means technology is infused into the business and technology experts sit at the table with our insurance experts. This requires taking an engineers of everything mentality to deliver the world-class customer experience that sets Chubb apart from our competition. By being a part of the digital-first journey at Chubb, you will be a transformer at the forefront of driving change in a multidisciplinary squad. Here is how some of our current transformers are describing how they feel about their experience in our squads: invigorated, heard, motivated, supported, and valued.

Want to know more about how you can be a part of the magic? Here’s what we’re looking for.

We are seeking a skilled and experienced Senior Data Engineer to join our Transformation Program team focused on revamping the core policy administration system for our Personal Lines Insurance division. In this role, you will be responsible for designing, developing, and maintaining data models that facilitate effective data management and support our business objectives. Your expertise will be crucial in transforming our data landscape to enhance operational efficiency, improve data quality, and support advanced analytics.

In this role, you will:

Design, develop, and maintain conceptual, logical, and physical data models for Personal Lines Insurance domain Collaborate with business stakeholders to understand data requirements and translate them into robust data models Create data architecture that aligns with the company’s data governance policies and industry best practices Ensure data models adhere to corporate data governance standards Participate in data quality initiatives, defining metrics, and addressing data quality issues within Personal Lines data Document data lineage and impact analysis to understand data flow within the policy administration system Act as a consultant for data-related questions, providing expertise on best practices and industry standards Collaborate with technical teams to implement database solutions that optimize performance and scalability Support testing and validation phases to verify that data models meet business and technical requirements Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field. Master’s degree preferredMinimum of 15 years of experience in data modeling within the insurance domain, preferably in Personal LinesProven experience in implementing large-scale transformation projects and/or core policy administration systemsExperience interpreting data from legacy data technologies (IMSDB, DB2)Hands on expertise in designing and building REST API data models using Swagger and PostmanProficiency in data modeling tools such as ER/Studio, Erwin Data Modeler, or similarStrong knowledge of SQL and experience with relational database management systems (e.g., Oracle, SQL Server) and NoSQL databases (like MongoDB, Cosmos etc.)Familiarity with Big Data technologies (e.g., Hadoop, Spark) and data warehousing concepts.Knowledge of data governance frameworks and data quality techniquesExperience with cloud-based data solutions (e.g., AWS, Azure, Google Cloud Platform) preferredKnowledge of Agile methodologies and experience working in Agile development environments preferred

 

The pay range for the role is $107,400 to $182,600. The specific offer will depend on an applicant’s skills and other factors. This role may also be eligible to participate in a discretionary annual incentive program.  Chubb offers a comprehensive benefits package, more details on which can be found on our careers website.  The disclosed pay range estimate may be adjusted for the applicable geographic differential for the location in which the position is filled.