Highly Skilled Data Platforms Engineer

1 week ago

Perth, Western Australia beBeeData Full time $120,000 - $175,000
Job Title: Specialist Data Projects

BHP is generating large amounts of data from different sources and scaling value through delivery of emergent AI solutions. As a Specialist Data & AI Platforms Engineer, you will play a hands-on role supporting the design, implementation, and improvement of core Data & AI Platforms and Technologies.

These platforms provide reusable, scalable, and sustainable capabilities for project teams and customer groups to solve business problems, optimize processes, and deliver value using Data, ML, and AI techniques.

  • Support the Analytics Platforms team across key technologies (Snowflake, DBT, Confluent, Astronomer, Monte Carlo, Informatica) to deliver a seamless data and analytics experience for users.
  • Collaborate with the Advanced Analytics Platform team to contribute to the design and implementation of enhancements across AWS, Azure, and Snowflake-based services (e.g., Snowflake Cortex, AWS Sagemaker, Azure ML, etc.).
  • Work closely with the Visualisation product team (Power BI) and the Data Governance team (Collibra, Purview) to support end-to-end platform delivery, ensuring governance and security requirements are met.
  • Assist in the delivery of project outcomes by providing platform knowledge and implementation support to project teams.
  • Contribute to automation, cost optimization, and platform monitoring efforts under the guidance of senior team members.
  • Follow and help maintain engineering standards, documentation, and guidelines.
  • Participate in Agile ways of working, contributing to sprint ceremonies, planning, and team rituals.
  • Collaborate with platform teams, architects, and delivery partners to understand requirements and support solution delivery.
Requirements:
  • 2+ years in data/cloud engineering or platform engineering roles with exposure to cloud-native PaaS and SaaS technologies.
  • Experience working with data and analytics platforms, such as enterprise data warehouses, data lakes, integration tools, or advanced analytics environments.
  • Exposure to source control, CI/CD workflows, and automation practices.
  • Some familiarity (professional or personal) with data science tools and libraries (e.g., Python, Jupyter, Pandas, TensorFlow, R).
  • Interest or experience in MLOps and data science operationalization practices.
  • An understanding of data integration, warehousing, or virtualization concepts.
  • Experience working in Agile and DevOps-oriented teams.
  • Strong communication and collaboration skills, with a willingness to learn from senior team members and share knowledge with others.

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