Enterprise Data Modeller – Hybrid, High-Impact Analytics

Sizewell C Company; Royal Borough of Greenwich; Hybrid; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Bonus: 5% annual incentive

Pension: Up to 7.5% employee / 15% (em

Job summary

Sizewell C is seeking a Data Modeller to design enterprise-level data models across domains, aligning with SZC standards and governance. You will collaborate with integration and engineering teams to capture source requirements and enable scalable data platforms.

Responsibilities include creating conceptual, logical and physical models, ensuring data quality and lineage, and maintaining documentation and metadata repositories for governance and discovery.

Qualifications

• Proven experience designing conceptual, logical and physical data models.
• Ability to translate business requirements into scalable data structures.
• Knowledge of data governance, metadata and data quality practices.
• Familiarity with data lakehouse architectures and modern data stacks.
• Experience with Azure data services and cloud platforms.

Responsibilities

• Design conceptual, logical and physical data models to support BI, analytics and operations.
• Capture data requirements and translate them into efficient data structures.
• Enforce modelling standards and governance across domains.
• Collaborate with data engineers to implement models in data warehouses and lakes.
• Maintain and update data models as business needs evolve.
• Document data elements, definitions and metadata.

Skills

Data modelling

Enterprise models

Azure data services

Medallion architecture

Version control

SQL

Data warehousing

Tools

ERwin

ER/Studio

PowerDesigner

SQL DBM

Job description

Sizewell C is seeking a Data Modeller to design enterprise-level data models across domains, aligning with SZC standards and governance. You will collaborate with integration and engineering teams to capture source requirements and enable scalable data platforms.

Responsibilities include creating conceptual, logical and physical models, ensuring data quality and lineage, and maintaining documentation and metadata repositories for governance and discovery.