Data Engineer (Snowflake / Databricks)

We are partnering with an enterprise client to accelerate their modern data platform roadmap and lakehouse delivery. We are seeking an experienced Data Engineer (Snowflake / Databricks) to represent our organisation and take technical ownership of designing, building, and optimising scalable ELT/ETL data pipelines and warehousing architectures.In this role, you will bridge heavy backend data engineering and analytics enablement within our client’s ecosystem. You will be instrumental in consolidating disparate data streams, building resilient Medallion architectures (Bronze/Silver/Gold), and deploying production-ready transformation pipelines using Snowflake, Databricks, dbt, and PySpark.🌏 Visa & Sponsorship OptionsAs the employer of record, we provide full visa and migration support for qualified engineering talent deployed to our clients:

• 482 On-Hire Sponsorship Transfers: Fully supported for qualified candidates currently in Australia on an existing 482 visa looking to transfer sponsorship to work with our clients.
• New 482 Visa Sponsorship: Available for qualified candidates meeting the commercial experience and technical requirements.
• Temporary & Working Visa Holders: Open to all working visa holders seeking a direct pathway to employer sponsorship.

Core Responsibilities

• Data Pipeline Engineering: Design, build, and maintain production-grade batch and real-time data pipelines using Snowflake, Databricks, Apache Spark/PySpark, and dbt.
• Lakehouse & Data Warehousing: Implement and manage Medallion/Lakehouse architectures, Unity Catalog governance, and performant dimensional models (Kimball star/snowflake schemas).
• Orchestration & DataOps: Automate workflow orchestration (e.g., Airflow, Prefect, Dagster) and enforce CI/CD, automated testing, and deployment standards across data platforms.
• Performance Tuning & Optimization: Optimize complex SQL queries, cluster utilization, partitioning, and warehouse compute configurations to ensure cost-efficiency and high throughput.
• Data Quality & Governance: Embed automated data validation frameworks, access controls, and data lineage across client data products.
• Stakeholder & Client Collaboration: Work closely with client product leads, analytics teams, and machine learning engineers to deliver reliable datasets for reporting and AI/ML initiatives.

Selection Criteria

• Core Platform Mastery: Deep commercial experience engineering data platforms using Snowflake and/or Databricks (Delta Lake / Unity Catalog).
• Programming & SQL: Advanced SQL capabilities paired with strong programming proficiency in Python or PySpark.
• Transformation & Modelling: Hands-on experience developing modular data models and transformations using dbt (data build tool) and dimensional design principles.
• Cloud Infrastructure: Practical experience integrating data services across AWS, Azure, or GCP.
• Location Requirements: Currently residing in Australia with valid work rights or eligibility for 482 visa sponsorship/transfer.

Preferred Qualifications (Nice to Have)

• Hands-on experience with streaming architectures (Kafka, Flink, Delta Live Tables).
• Snowflake SnowPro or Databricks Certified Data Engineer credentials.
• Experience with Infrastructure as Code (Terraform) for data platform provisioning.