Profile Summary

This role is required predominantly to help build out the DSM enterprise data model (DSMD) on a use case basis. The data modeler designs, implements, and documents data architecture and data modeling solutions, which include the use of relational and dimensional databases. These solutions support enterprise information management, business intelligence, machine learning, data science, and other business interests. This is a technical role with the responsibility to lead the development, validation, publishing, and maintenance of logical and physical data models required to create data marts in the DAP to drive reuse and flexibility in the use of data within DSM. The creation of the DSMD will be a platform to help decommission legacy silo data sets and reporting in DSM. Leverage and enforce standards for data naming conventions to ensure consistency and reuse of data models. This role is expected to leverage their knowledge of data modeling best practices along with cross-industry data expertise and data modeling tool expertise.

The successful candidate will:

be responsible for the development of the conceptual, logical, and physical data models, the implementation of DSDM on the DAP including the transition from raw, to refined to consumption.
oversee and govern the expansion of existing DSDM and the optimization of data query performance via best practices. The candidate must be able to work independently and collaboratively
perform reverse engineering of physical data models from databases and SQL scripts where required. This is the least preferred approach as defined in the ingestion of source systems and reports developed within the DNS program.
validate business use cases for accuracy and completeness of proposed data models
Be able to work in a squad in line with the DSM agile ways of working within the broader DNA program
Be able to work independently and collaboratively with the Enterprise Architects
Maintaining data modeling and database design processes.
Assist with creating a robust and effective data ops process for data model versioning and integration of project data models into production in support of the build-out of the DSM
have experience in AWS-based data modeling tools

The approach requires not just an immediate solution but:

a strategic solution which takes account of detailed drill-down to the lowest level,
a practical way to join data in different entities to ensure performance is optimal and satisfactory to users
drives optimal re-use of data set for dashboard configuration
ability to manage and build conceptual, logical, and physical models in data modeling tooling
experience developing other data modelers
an understanding of data management principles and the integration of data models to understand and manage change

Data Modeller