Data Modeller

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

EY GDS – Data and Analytics (D&A) – Techno-Functional Data Modeller and Analyst – Insurance (Senior Con.)

As part of our EY-GDS D&A (Data and Analytics) team, we help our clients solve complex business challenges with the help of data and technology. We dive deep into data to extract the greatest value and discover opportunities in key business and functions like Banking, Insurance, Manufacturing, Healthcare, Retail, Manufacturing and Auto, Supply Chain, and Finance

The opportunity

We are seeking an experienced Techno-Functional Data Consultant with strong expertise in Insurance Data Management, Data Modelling, Business Analysis, Reporting & Analytics, and Data Migration. The ideal candidate will act as a bridge between business stakeholders and technical teams, translating business requirements into scalable data solutions that support operational, analytical, and reporting needs.

The role requires hands-on experience in Data Vault modelling, dimensional modelling, data analysis, reporting tools, and insurance data domains. Experience working within the UK Insurance Market is highly desirable.

Your Key Responsibilities

Business Analysis & Stakeholder Management

• Collaborate with business users, SMEs, architects, and delivery teams to gather and document functional and data requirements.
• Conduct workshops to understand business processes, reporting needs, and data challenges.
• Translate business requirements into functional specifications, data models, and reporting solutions.
• Perform impact assessments and support change management activities.
• Act as the key liaison between business and technology teams.

Data Analysis & Data Management

• Perform data profiling, gap analysis, and source-to-target data mapping.
• Analyse large and complex datasets to identify data quality issues and transformation requirements.
• Define business rules, validation rules, and reconciliation requirements.
• Support data governance, metadata management, and data lineage initiatives.
• Ensure consistency and integrity of data across operational and analytical platforms.