Data Engineer (Google Cloud Platform)

About the role

An experienced Data Engineer with strong Google Cloud Platform (GCP) expertise is sought to design, develop and maintain scalable, secure and reliable data solutions in a complex enterprise environment. The role is open to candidates based in Queensland, Western Australia, the ACT, Victoria, New South Wales, the Northern Territory, South Australia and Tasmania. A current Negative Vetting Level 1 (NV1) security clearance is mandatory.

Key responsibilities

• Design, develop and maintain scalable batch, micro-batch and real-time data ingestion, transformation and curation pipelines.
• Translate solution designs, technical specifications and data contracts into production-ready data solutions.
• Apply data warehousing, dimensional modelling, data quality and governance principles, including metadata, lineage, classification, retention and security.
• Develop and optimise pipelines using GCP services such as BigQuery, Dataflow, Apache Beam, Cloud Storage, Cloud Composer and Pub/Sub.
• Implement DataOps practices, including CI/CD, infrastructure automation, automated testing, monitoring, alerting and release management.
• Build and maintain automated unit, integration, reconciliation and data quality tests.
• Collaborate with technical teams and business stakeholders, identify technical risks and recommend improvements.
• Contribute to technical analysis, estimation, solution planning, standardisation and continuous improvement.
• Develop reusable data products and trusted datasets to support advanced analytics, machine learning and generative AI.

Required profile

• At least three years of data engineering experience, including at least two years designing, building and operating production data solutions on GCP.
• Demonstrated experience delivering and supporting production-grade data pipelines in complex enterprise environments.
• Ability to translate business and technical requirements into scalable, secure and maintainable solutions.

Required skills

• Strong hands-on experience with BigQuery, including data modelling, partitioning, clustering, query optimisation, cost management and workload monitoring.
• Scalable batch and streaming pipelines with Dataflow and Apache Beam.
• Data warehousing, dimensional modelling and data quality practices.
• SQL and Python for developing, testing and optimising pipelines.
• DataOps: CI/CD, infrastructure as code, automated testing and monitoring.
• Terraform, Git-based source control and CI/CD tooling such as Cloud Build or GitHub Actions.
• Cloud security, data governance, metadata management and data lifecycle principles.