How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Role Title: Data Modeler
Work mode: Hybrid
Number of days a week worked in the office: but on an average 3 Days/week
Contract duration: 6 months
Location: London
JOB DETAILS
We are seeking an experienced Data Modeler to support a modern enterprise data platform for a global specialty insurance organization. The ideal candidate will be responsible for designing, developing, and maintaining conceptual, logical, and physical data models that support business-critical insurance data domains including Underwriting, Claims, Policy Administration, Reinsurance, Finance, Actuarial, and Regulatory Reporting. The role requires strong expertise in data modeling principles, insurance data structures, dimensional modeling, data governance, and metadata management. The candidate will work closely with business stakeholders, architects, business analysts, and data engineering teams to ensure scalable, high-quality, and governed data solutions that support reporting, analytics, and regulatory requirements.
Your Role & Responsibilities
- Design and maintain conceptual, logical, and physical data models for enterprise data platforms.
- Collaborate with business stakeholders, Business Analysts, and Architects to understand data requirements and business processes.
- Develop scalable and optimized data models to support reporting, analytics, and operational use cases.
- Translate business requirements into robust data structures and data models.
- Define and maintain enterprise data standards, naming conventions, and modeling best practices.
- Create and maintain data dictionaries, metadata repositories, and business glossaries.
- Support data governance initiatives including lineage, data quality, and metadata management.
- Work closely with Data Engineers to implement data models within Snowflake and cloud-based data platforms.
- Design dimensional models (Star Schema, Snowflake Schema) for analytical and reporting solutions.
- Analyze source systems and define source-to-target mappings (STTM).
- Ensure data consistency, integrity, and quality across multiple data domains.
- Support data reconciliation, validation, and impact analysis activities.
- Collaborate with data governance teams to ensure compliance with security, privacy, and regulatory requirements.
- Review and optimize existing data models for performance, scalability, and maintainability.
- Support implementation of Data Mesh and modern data architecture principles.
- Participate in architecture reviews and provide recommendations on data design decisions.
- Work with lineage and cataloging tools such as Manta, Data world, or similar platforms.
- Support Agile delivery processes including sprint planning, backlog grooming, and design reviews.
- Document data architecture, data flows, business rules, and transformation logic.
- Partner with global teams to ensure successful delivery of enterprise data initiatives.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Required
- Design, develop, and maintain conceptual, logical, and physical data models for enterprise data platforms.
- Collaborate with Business Analysts, Architects, and stakeholders to translate business requirements into scalable data models.
- Develop and optimize dimensional, Data Vault, Star Schema, and Snowflake Schema models to support analytics and reporting.
- Perform source system analysis, data mapping, and create Source-to-Target Mapping (STTM) specifications.
- Ensure data quality, consistency, integrity, lineage, and traceability across the data landscape.
- Partner with Data Engineering teams to implement data models on Snowflake and cloud-based data platforms.
- Establish and maintain data standards, metadata, business glossaries, and governance frameworks.
- Support data governance initiatives, including metadata management, data cataloging, compliance, and security requirements.
- Leverage tools such as Data world, Manta, Collibra, or similar platforms for data lineage and metadata management.
- Work with Azure, AWS, or GCP data ecosystems and support modern ETL/ELT integration patterns.
- Contribute to architecture reviews, data modernization initiatives, and enterprise data transformation programs.
- Collaborate with Agile teams and actively participate in sprint planning, backlog refinement, design reviews, and solution delivery.
- Model and manage insurance data domains including Policies, Claims, Underwriting, Premiums, Reinsurance, Finance, Actuarial, Risk, and Regulatory Reporting.
- Ensure compliance with insurance industry standards, data governance controls, auditability, reconciliation, IFRS 17 reporting, and regulatory requirements.
- Provide data modeling expertise for specialty insurance and reinsurance platforms, supporting business stakeholders through scalable, governed, and business-aligned data solutions.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Preferred
- Snowflake Certification or equivalent cloud data warehouse experience.
- Experience with Data Vault 2.0 methodology.
- Exposure to Azure Data Factory (ADF), Databricks, or similar cloud data technologies.
- Experience with enterprise data catalog and governance tools.
- Knowledge of Data Mesh architecture principles.
- Understanding of Master Data Management (MDM).
- Experience working on cloud migration and modernization programs.
- Familiarity with CI/CD and DevOps practices for data platforms.
- Experience working in global delivery and client-facing environments.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location