Data Modeller

Spire Healthcare

Experts In Private Healthcare. Over 3,700 Consultants and 38 Award Winning Private Hospitals · Long Visiting Hours · Wide Range of Treatments

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Posted 1 month ago

Key Responsibilities

Design, build and maintain: Conceptual, logical and physical data models to meet business needs of strategic integration via FHIR and analytics OMOP standards with dimensional modelling.
Define, uphold and validate: Data modelling standards, principles and best practices are met and aligned with business requirements.
Build, manage and educate: Data catalogues, working closely with data architects and business users to setup and refer to data catalogues.
Data Quality & Security: Work closely with data architects and data engineers to maintain appropriate security and address data quality issues
Collaboration & Insights: Partner with analysts, scientists, and business stakeholders to align data modelling alongside delivery of actionable insights and advanced analytics, whilst promoting self service.
Align modelling to data roadmap: Based on a data domain approach, work alongside the team to develop and plan data modelling to the data and integration roadmap needs, as well as delivery demands.

Required Experience & Skills

4+ years in data modelling roles.
Proven expertise translating business requirements into conceptual, logical and physical data models
Expertise in data model tools such as ERwin, LucidChart or SQL based platforms to create and manage data structures.
Proficient coding skills in SQL and database design.
Experience with CI/CD practices and tools such as Azure DevOps, and data quality frameworks.
Familiarity with Azure security tools (RBAC, Key Vault) and configuration management.

Desirable Qualifications & Skills

Certifications: Azure Foundations or Data Scientist Associate, Power BI Data Analyst Associate.
Experience with FHIR/OMOP data standards, Microsoft Fabric, Microsoft Purview and Power BI would be highly beneficial.

Personal Attributes

Strong collaboration and communication skills, both inside and outside of technology.
Passion for quality, innovation, and continuous learning.
Disciplined focus on improving our data landscape and data modelling practices to minimize overhead of maintenance and maximise re-use.

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

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