Cloud Data Engineering Specialist

2 weeks ago

Sydney, New South Wales, Australia beBeeDataEngineer Full time $118,878 - $168,828
Job Overview

As a Data Engineer, you will design and build scalable data pipelines using modern tools and technologies.

  • Design and develop data engineering solutions that support business growth and analytics requirements.

Take ownership of troubleshooting and resolving issues in data workflows to ensure timely and reliable data delivery.

Collaborate with senior engineers and analysts to develop and maintain data models that support analytics and product needs.

Work with Databricks and Snowflake environments to support ingestion, transformation, and modeling of data.

Stay up-to-date with emerging trends and best practices in cloud data engineering.

Key Responsibilities
  • Develop and maintain data pipelines using modern data stack tools (dbt, Airflow, Python).
  • Collaborate with cross-functional teams to clarify requirements and deliver solutions.
  • Communicate progress, challenges, and technical considerations to both technical and non-technical stakeholders.
  • Maintain accurate documentation for pipelines, workflows, and solutions.
  • Support ongoing platform improvements and contribute to scaling best practices across environments.

Apply your knowledge of data modelling principles (Kimball, Data Vault, etc.) to ensure scalable and performant solutions.

Partner with data analysts and engineers to translate business requirements into technical solutions.

Requirements
  • Experience with data engineering and pipeline development.
  • Strong understanding of modern data stack tools (dbt, Airflow, Python).
  • Ability to communicate complex technical concepts to non-technical stakeholders.
  • Knowledge of data modelling principles and best practices.

We are looking for someone who is passionate about staying up-to-date with emerging trends and best practices in cloud data engineering.

Benefits
  • Opportunity to work with a talented team of engineers and analysts.
  • Chance to contribute to the development and maintenance of data models and pipelines.
  • Professional development opportunities and training programs.

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