Data Modeler 181596

Key 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.

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)

Nice-to-Have Qualifications

• 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.

The Necessary Skills

• 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 data quality and the ability of users to easily consume data.
• Experience with cloud native data warehousing and data lake solutions using Snowflake is a plus.
• Exposure to working with a Data Scientist team and knowledge of Feature Engineering is a plus.
• Experience of database design through the full development lifecycle is a plus: from requirements to conceptual, logical and physical data model design and implementation.

The Value Delivered

• Key participant in helping to build a Data Driven Culture.
• Designing and delivering data mapping specifications for large reporting platforms.
• Writing and maintaining business rules using SQL logic
• Completing research to identify effective data designs, new tools and methodologies for data analysis.
• Collaborating with DBAs, architects, and subject area owners to create scalable data models.
• Gathering and incorporating feedback from Product Owners, Data Architects, and Data Engineers to aid in the design of the product.
• Guiding engineers in the best approach to assembling large, complex data sets that meet functional / non-functional business requirements.
• Being candid and honest in all discussions to ensure the best outcomes.
• Driving innovation and experimentation within the data organization.