Data Modeler

Position Summary

Data Modeler is responsible for designing, developing, and maintaining data models that support business operations, analytics, and reporting systems. This role ensures data structures are efficient, scalable, and aligned with organizational goals.

• Design and maintain conceptual, logical, and physical data models for both transactional (OLTP) and analytical (OLAP) systems.
• Develop data models aligned to insurance domains including policy lifecycle (quote, issue, servicing), coverages, premiums, billing, and claims.
• Create and deliver data mapping specifications and transformation logic for large-scale reporting and integration platforms.
• Define and enforce data modeling standards, naming conventions, and best practices across the organization.
• Partner with Product Owners, Data Architects, Engineers, and Business stakeholders to translate requirements into robust data solutions.
• Design canonical and enterprise data models to promote data consistency, reuse, and interoperability across systems.
• Ensure data quality, integrity, and lineage are maintained throughout the data lifecycle.
• Guide engineering teams in assembling large, complex datasets that meet functional and non-functional requirements.
• Optimize data structures for performance, scalability, and maintainability.
• Contribute to data governance efforts, including metadata management, lineage documentation, and stewardship.
• Identify opportunities to modernize legacy data models and drive innovation in design approaches.
• Support Agile delivery by actively participating in sprint planning, design reviews, and backlog refinement.

• Bachelor's Degree (or equivalent experience) in Computer Science, Engineering, Information Systems, or a related field.
• 8+ years of experience in data modeling, database design, or data architecture.
• Strong hands-on experience with:
• Relational databases (Oracle, PostgreSQL, MySQL)
• SQL development and performance tuning
• Data modeling methodologies (Normalization, Kimball, Inmon)
• Proven experience working with:
• Policy Administration Systems (OIPA preferred, or Guidewire/Duck Creek)
• Highly transactional (OLTP) environments
• Expertise in designing:
• Dimensional models (Star/Snowflake schemas)
• Normalized operational models
• Canonical / enterprise data models
• Experience with data modeling tools such as Erwin, SAP Power Designer, Hackolade, or equivalent.
• Strong understanding of:
• Data lifecycle (creation to consumption)
• Data integration patterns (ETL/ELT)
• Data warehousing and operational data stores (ODS)

• Experience with Insurance Domain Modeling.
• Experience with cloud-native data platforms such as Snowflake, Azure Synapse, or AWS Redshift.
• Exposure data lakes and lakehouse architectures.
• Familiarity with data governance frameworks, data lineage, and metadata tools.
• Experience supporting Data Science / ML teams, including feature engineering concepts.
• Knowledge of insurance regulatory and reporting requirements.
• Experience working with large-scale enterprise data ecosystems.

• Excellent communication skills including written, verbal, and technology illustrations.
• The desire and aptitude for learning new technologies to solve problems and formulate recommendations.
• A proven track record of working in reciprocal teams to deliver high quality data solutions in an agile environment.
• A fearless approach to challenge the legacy design and seek opportunities to design better models and better code.
• A passion for data analysis with the ability to navigate and master complex transactional and warehouse databases.
• A love working with data and are confident in your SQL skills.
• Technically adept in learning new technologies and architectural methodologies and adapting our practices to support frequent changes in the technical environment.
• A strong interest in playing a technical data steward role and across our business and technology partners to understand and detail our data, appropriate data usages and helping [BU] derive the maximum value from its data.
• The ability to take a broad view of data from creation to consumption and understand how data design patterns affect…