Data Foundation Specialist

1 week ago

Sydney, New South Wales, Australia beBeeDataEngineer Full time $100,000 - $150,000
Key Position:

The Data Services team oversees the design and migration of data products onto the DAP platform. This platform is transitioning to cloud technologies like Azure and Databricks.

This data foundation serves analytics, BI, and machine learning use cases across various business units, enterprise functions, and key value chains in Claims and Underwriting.

Major Responsibilities:
  • Contribute to strategy development for continual data foundation growth.
  • Validate, analyze, and manipulate data from operational systems and the data platform, collaborating with stakeholders, analysts, and Data Scientists to fulfill data requirements.
  • Enhance data platform capabilities by sourcing and integrating additional data as needed, creating feature engineering frameworks, and developing scalable data engineering assets.
  • Assess feature importance and oversee the production of data features.
  • Develop, test, and document data models and ETL tools for robust and scalable data assets supporting reporting and analytics.
  • Deploy data assets to production and provide level 3 support for assets in production.
  • Identify, source, and integrate new data to tackle challenges and improve the data platform.
  • Design and implement process improvements such as automation, optimization of data delivery, and infrastructure redesign for greater scalability.
  • Conduct peer code reviews and foster a DevOps culture within the team.
  • Implement solutions adhering to architecture best practices.
  • Contribute to the development of a best-practice Data and Analytics platform, leveraging cutting-edge technologies, and designing data pipelines for faster decision-making.
About You

You will have proven experience in data engineering, solution designs, and delivery. Strong communication skills are essential for articulating, presenting, influencing, and negotiating with key stakeholders. Knowledge and understanding of the end-to-end data engineering lifecycle are required.

Key qualifications include:

  • 4+ Years of experience in Cloud data analytics technologies: Databricks, Azure ADF.
  • 3+ years' experience with real-time and streaming technology (i.e. Azure Event Hubs, Azure Functions Kafka, Spark Streaming).
  • Solid experience using MS Stack: Microsoft SQL Server, SSIS, SSRS, SSAS, Power BI and SQL.
  • Experience working MongoDB, Graph databases, programming languages (R, Python, Spark ETL SQL VStudio), CI/tools (Bamboo, or similar), GitHub, Jira and Confluence.
  • Passionate about learning new data tools and best practices.
  • Experience working in Agile teams, able to collaborate and work independently.
  • Strong working knowledge of the end-to-end data management lifecycle.

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