Data Modeler

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 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. Skills An experienced, driven expert in a broad set of data capabilities such as: Data design patterns and optimization across disparate mediums within a Cloud-based environment (preferably AWS) such as large object file systems (AWS S3), RDBMS and columnar databases A strategic thinker who can define modelling patterns for various layers of a data environment balancing storage vs. compute costs, optimized for as broad a set of use cases as possible Extensive data modelling experience, from conceptual to physical Expertise in different modelling methodologies such as 3NF, Dimensional, Data Vault Expertise in building cloud data warehouses using Kimball, preferably using AWS Redshift Knowledge/experience of building queries and MI outcomes utilizing data visualization technologies (eg, Tableau) Qualifications in RDBMS design and/or administration and in AWS architecture (at least one of these). Awareness of data governance and data ethics in the production of automated modelling. Proven track record of delivery of modelling designs/approaches in large scale data environments. Evidence of broad stakeholder management from senior business level down to analyst. Experience in or extensive exposure to MI/BI use cases, data exploration and analysis. Experience within predictive modelling/Data Science would be an advantage. Experience in defining and delivering data monitoring across a large platform as well as establishing governance forums, processes and guardrails to ensure compliance with standards.

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