Data Modeler

Key participant in building a data-driven culture, designing scalable data models, and delivering data mapping specifications for large reporting platforms.

Responsibilities

• 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.
• Write and maintain business rules using SQL logic.
• Complete research to identify effective data designs, new tools, and methodologies for data analysis.
• Collaborate with DBAs, architects, and subject area owners to create scalable data models.
• Gather and incorporate feedback from Product Owners, Data Architects, and Data Engineers to aid in product design.
• Guide engineers in assembling large, complex datasets that meet business requirements.
• Drive innovation and experimentation within the data organization.

Requirements

• 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 PowerDesigner, Hackolade, or equivalent.
• Strong understanding of:
  • Data lifecycle (creation to consumption)
  • Data integration patterns (ETL/ELT)
  • Data warehousing and operational data stores (ODS)

Nice-to-Have Qualifications

• Experience with Insurance Domain Modeling.
• Experience with cloud-native data platforms such as Snowflake, Azure Synapse, or AWS Redshift.
• Exposure to 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.
• Experience of database design through the full development lifecycle: from requirements to conceptual, logical, and physical data model design and implementation.

Skills

• Excellent communication skills including written, verbal, and technology illustrations.
• Desire and aptitude for learning new technologies to solve problems and formulate recommendations.
• Proven track record of working in reciprocal teams to deliver high-quality data solutions in an Agile environment.
• Fearless approach to challenging legacy designs and seeking opportunities to improve models and code.
• Passion for data analysis with the ability to navigate and master complex transactional and warehouse databases.
• Strong SQL skills.
• Technically adept in learning new technologies and architectural methodologies.
• Strong interest in playing a technical data steward role across business and technology partners.
• Ability to take a broad view of data from creation to consumption and understand how data design patterns affect data quality and usability.