About the role

JD – Data Modeller

About the role:

The Data Modeller designs fit-for-purpose conceptual, logical, and physical data models in-line with business requirements to serve analytical, business intelligence, and operational use cases primarily within the data platform.

Key accountabilities & responsibilities:

Elicit, analyze, and document data requirements in support of analytical, business intelligence, warehousing and other business use cases for the data platform using a range of techniques including source data analysis, documentation, interviews, and data modelling workshops
Create and maintain data models appropriate to the need and that conform to data modelling standards.
Produce and maintain metadata (including relationships, calculation logic etc.) and documentation to accompany data models using data modelling tools where appropriate.
Ensure models will provide data structures that meet the required range of non-functional requirements including performance, extensibility, change capture (SCD etc), understandability, and maintainability.
Create and maintain specifications for data transformation in both documentation (Source To Target Mapping) and scripting.
Agree artefacts, models, documentation, and scripts with relevant business owners, stewards, SMEs, and technical stakeholders.
Test models and transformation scripts to ensure they meet requirements.
Perform day-to-day data model and script maintenance to tune performance, respond to changes, and in support of IT issues.
Advise data engineers, visualization developers, and other consumers of models and data specifications in the interpretation of data models and structures and the understanding of data requirements.
Contribute to the definition of data dictionaries, and business glossaries.
Partner with and support adjacent teams.

Knowledge/Experience

Proven knowledge of physical and logical data modelling in a data warehouse environment including the successful creation of conformed dimensional models from a range of legacy source systems alongside modern SaaS/Cloud business applications
Experience within a similar role within insurance (ideally health insurance) or similar complex and regulated industry, and able to demonstrate a sound working business knowledge of its operation.
Experienced at capturing technical and business metadata including being able to elicit and create sound definitions for entities and attribute.
Practiced and able to query data from source or raw data and reverse engineer an underlying data model and data definitions.
Experienced in writing scripts for data transformation using SQL, DDL, DML, and Pyspark.
Good knowledge and exposure to software development lifecycles and good engineering practices
Can demonstrate a good working knowledge of data modelling patterns and when to use them.

Technical skills

Entity relationship, dimensional, and NOSQL modelling: as appropriate to data warehousing, business intelligence, and analytical approaches using IE or other common notations.
Scripting: SQL, DDL, DML, and Pyspark scripting
Data modelling tools: ERWIN, and Visio data modelling/UML tool
Ideally: Azure Data Factory, Azure Dev Ops, and Databricks