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Review existing domain data mart models/architecture to ensure that they meet the needs of our data strategy and are optimized to support our key analytical use cases.

* Design and develop remediated designs/models and work with engineering and analytical stakeholders across the different domains to create backlogs for model standardization and improvements.

* Ensure storage and consumption approaches/designs deliver maximum efficiency, with a focus on balancing storage and compute costs optimally.

* Produce and maintain modelling and design guardrails, standards and processes and integrate these with wider data management and engineering governance, for example:

* Data query performance

* Data table structures

* Partitioning of data across S3 and other object stores

* Data Lifecycle Management – especially in AWS S3 and other cloud object file systems where storage costs are key

* Where and how business logic is developed, tested, approved and embedded

* Who can create permanent or semi-permanent data, where it can be created and how it is managed

* How data is presented and accessed

* Review data modelling and technical approaches to ensure that they are consistent and of high quality