Data Modeling Lead (DT)

Description

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 Data Modeling Lead 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

Qualifications

Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field. Master’s degree preferred

Minimum of 15 years of experience in data modeling within the insurance domain, preferably in Personal Lines

Proven experience in implementing large-scale transformation projects and/or core policy administration systems

Experience interpreting data from legacy data technologies (IMSDB, DB2)

Hands on expertise in designing and building REST API data models using Swagger and Postman

Proficiency in data modeling tools such as ER/Studio, Erwin Data Modeler, or similar

Strong 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 techniques

Experience with cloud-based data solutions (e.g., AWS, Azure, Google Cloud Platform) preferred

Knowledge of Agile methodologies and experience working in Agile development environments preferred

The pay range for the role is $160,000 - $190,000. 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.