Chief Data Platform Architect

1 day ago

Melbourne, Victoria, Australia beBeeDataPlatformManager Full time $139,146 - $164,602

Establishing a cutting-edge data platform is crucial for organisations seeking to harness the power of data-driven decision-making. Our ideal candidate will lead the delivery of a modern, scalable enterprise-wide data platform to meet the analytical and operational needs of our organisation.

About the Role

We are seeking a seasoned professional with technical leadership expertise to oversee the optimisation of our data analytics platform architecture and tooling, ingestion frameworks, transformation pipelines, and data governance frameworks. The successful candidate will drive improvements in our data platform infrastructure, ensuring seamless integration with internal and external data sources while maintaining data security and privacy.

This role combines strategic leadership with technical know-how, requiring the ability to partner with internal stakeholders to align the data analytics platform with governance, architectural, security, and best-practice standards. Key responsibilities include:

  • Establishing, scaling, leading, and managing a team of data specialists, including data engineers, data platform administrators, and data architects, to support data platform development and operations.
  • Reviewing the current state of our Microsoft Azure cloud-based data analytics platform, including documentation, to identify gaps and opportunities for uplift and inform a roadmap to grow our data capability in line with our Data Strategy – this includes data science and AI applications.
  • Evaluating and uplifting technical documentation on data analytics platform design and operations to ensure alignment with relevant policies and procedures and best practice and standards.
  • Optimising data analytics platform architecture and operations in line with business needs to make data more accessible and create and maintain reliable and reusable data ingestion pipelines and transformation frameworks to integrate and manage internal and external data, while maintaining data security and privacy.
  • Monitoring and enhancing platform performance and usage to ensure efficient resource consumption and drive continuous improvement in platform reliability, data pipeline performance, and job orchestration.
  • Implementing observability frameworks (e.g., quality checks, lineage, logging) to proactively detect and resolve data issues and address data quality.
  • Partnering with key internal and external stakeholders to deliver a fit-for-purpose enterprise-wide data analytics platform infrastructure with technical capabilities that are scalable, flexible, and cost-effective to meet changing organisational needs and priorities.

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