Data Modeller

The Expertise Required

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)
• Experience with Insurance Domain Modeling.
• Experience with cloud-native data platforms such as Snowflake, Azure Synapse, or AWS Redshift.
• Exposure data lakes and lake house 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.
   

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.