About the Company:
Our client an iconic Australian organisation providing products and services to millions of customers. They are pioneering, progressive and are leaders in their field.
About the Role:
Provides technical leadership in data modelling and engineering, creating and maintaining physical data models, definitions, and mappings for the organisation's Data Platform and BI tools. As custodian of the Lakehouse data models, he ensures structure, quality, accuracy, and accessibility of data, enabling insights for decision-making and supporting both projects and BAU.
Responsibilities:
* Define, develop, and maintain data analytics solutions, including the design of impactful data visualisations that enable actionable insights.
* Design, build, test, and deploy solutions across multiple environments using Inmon, 3NF, and Kimball methodologies, while creating and maintaining comprehensive technical documentation.
* Conduct system reviews and performance analysis to ensure that data pipelines and data stores are efficient, reliable, and optimised for scalability.
* Perform detailed business and data analysis, including data profiling, mapping, and documentation of results into business matrices and requirement specifications.
* Analyse user requirements to design functional specifications for front-end applications, providing proactive improvements that align with business objectives.
* Design and maintain conceptual, logical, and physical data models, along with data mappings, transformations, definitions, and quality standards, ensuring alignment with Enterprise Data Models.
Skills and Experience:
* Extensive expertise in data architecture, data modelling, and data warehouse design, with deep knowledge of data lake concepts, data structures, operations, management, and governance.
* Proven proficiency in the Microsoft Azure Data Platform, with a strong focus on Azure Databricks, complemented by hands-on experience using Sparx EA, Erwin, or equivalent modelling tools.
* Strong track record in Star Schema design and implementation to optimise data accessibility and analytics.
* In-depth knowledge of metadata management to ensure consistency and integrity across data models.
* Demonstrated ability to translate complex business requirements into robust data solutions, delivering physical data models aligned with conceptual and logical reference architectures.
* Practical understanding of Git-based version control and DevOps practices, with exposure to Azure DevOps preferred.
For a confidential discussion, please contact Pravin on
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