We are looking for an experienced Snowflake + dbt Engineer to design, develop, and maintain scalable data transformation pipelines using contemporary cloud data technologies. The ideal candidate will have strong SQL and dbt Core expertise, hands-on experience with Snowflake and Apache Airflow, and a solid understanding of AWS services and Python-based data ingestion.

The role involves building reliable, high-quality, business-ready datasets while ensuring data accuracy, pipeline performance, and effective CI/CD processes.

Key Responsibilities

- Design, develop, and maintain data transformation pipelines using Snowflake, dbt Core, and SQL.
- Implement incremental data loads, deduplication logic, and efficient data transformation strategies in Snowflake.
- Develop, maintain, and troubleshoot Apache Airflow DAGs for data pipeline orchestration.
- Build Python-based data ingestion processes to extract, process, and load data from multiple sources.
- Utilize AWS services, including S3, Lambda, ECS, MWAA, Glue Data Catalog, Lake Formation, SQS, and Event Bridge.
- Implement CI/CD pipelines using GitHub Actions to automate testing and deployment workflows.
- Develop data quality tests using YAML configurations and maintain source-to-target mapping documentation.
- Process structured and fixed-format financial data files, ensuring data integrity and consistency.
- Monitor pipeline performance, troubleshoot failures, and optimize data processing workflows.




- Collaborate with data engineers, analysts, and business stakeholders to deliver reliable and well-documented datasets.

Mandatory Skills

- Strong SQL skills and hands-on experience with Snowflake.
- Proficiency in dbt Core for data transformation and modeling.
- Experience implementing incremental loads, deduplication, and data transformation logic.
- Hands-on experience developing and debugging Apache Airflow DAGs.
- Working knowledge of AWS data and orchestration services.
- Experience with GitHub Actions, CI/CD, and version control using Git.
- Knowledge of data quality testing, YAML configurations, and source-to-target mapping documentation.
- Experience handling structured or fixed-format financial data files.

Good-to-Have Skills

- Experience with AWS Managed Workflows for Apache Airflow (MWAA).
- Familiarity with AWS Glue Data Catalog and Lake Formation.
- Experience with event-driven architectures using SQS and Event Bridge.
- Understanding of data warehouse design, data modeling, and pipeline optimization.
- Experience working with financial or other business-critical datasets.
- Strong analytical and problem-solving skills.
- Attention to data quality, accuracy, and reliability.
- Ability to troubleshoot complex data pipeline issues.Effective communication and collaboration skills.
- Ability to work independently in a contract-based delivery environment.

📌 Data Engineer (Sydney)
🏢 PRIMUS Global Solutions (PRIMUS UK & Europe)
📍 Sydney