Senior Data Engineer – Credit Risk Platform (AWS | PySpark | dbt) Location: Sydney (Hybrid)
Type: Contract 

Overview We’re partnering with a leading financial services organisation undergoing a major transformation of its Credit Risk Technology platform.

This role will play a key part in designing and delivering a modern data platform, migrating away from legacy systems and building out a scalable, cloud-native architecture aligned to a medallion (bronze/silver/gold) data model.

You’ll work at the intersection of data engineering, platform design, and risk analytics, helping drive the target-state architecture and uplift engineering standards across the platform.

Key Responsibilities

• Design and build scalable data pipelines and data models to support credit risk use cases
• Contribute to the migration from legacy platforms into AWS-based data architecture
• Implement and optimise PySpark-based ETL pipelines
• Develop and manage workflows using Airflow and/or Control-M
• Build transformations using dbt and support downstream analytics/reporting use cases
• Work with distributed query engines such as Starburst Presto
• Contribute to the rollout of AWS Glue as part of the target-state platform
• Collaborate with risk, data, and platform teams to ensure data quality, lineage, and governance
• Support the adoption of medallion architecture (bronze/silver/gold layers)
• Drive best practices across performance optimisation, testing, and CI/CD

Key Skills & Experience

• Strong hands-on experience with Python and PySpark
• Advanced SQL and data modelling expertise
• Experience building pipelines on AWS (S3, Glue or similar)
• Workflow orchestration using Airflow and/or Control-M
• Experience with dbt or similar transformation frameworks
• Exposure to distributed query engines (e.g. Presto / Starburst)
• Solid understanding of data warehousing and lakehouse architectures
• Experience working on large-scale data migration or platform modernisation initiatives

Nice to Have

• Experience in credit risk, financial services, or regulated environments
• Exposure to Redshift or similar MPP databases
• Familiarity with data governance and lineage tooling
• Experience working within medallion or lakehouse architectures

Why Join

• High-impact role driving a strategic credit risk platform transformation
• Opportunity to shape a modern AWS data ecosystem from the ground up
• Work within a large-scale, enterprise data platform uplift program
• Strong engineering culture with a focus on scalability, performance, and best practice

Senior Data Engineer – Credit Risk Platform (AWS | PySpark | dbt) Location: Sydney (Hybrid)
Type: Contract / Permanent

Overview We’re partnering with a leading financial services organisation undergoing a major transformation of its Credit Risk Technology platform.

This role will play a key part in designing and delivering a modern data platform, migrating away from legacy systems and building out a scalable, cloud-native architecture aligned to a medallion (bronze/silver/gold) data model.

You’ll work at the intersection of data engineering, platform design, and risk analytics, helping drive the target-state architecture and uplift engineering standards across the platform.

Key Responsibilities

• Design and build scalable data pipelines and data models to support credit risk use cases
• Contribute to the migration from legacy platforms into AWS-based data architecture
• Implement and optimise PySpark-based ETL pipelines
• Develop and manage workflows using Airflow and/or Control-M
• Build transformations using dbt and support downstream analytics/reporting use cases
• Work with distributed query engines such as Starburst Presto
• Contribute to the rollout of AWS Glue as part of the target-state platform
• Collaborate with risk, data, and platform teams to ensure data quality, lineage, and governance
• Support the adoption of medallion architecture (bronze/silver/gold layers)
• Drive best practices across performance optimisation, testing, and CI/CD

Key Skills & Experience

• Strong hands-on experience with Python and PySpark
• Advanced SQL and data modelling expertise
• Experience building pipelines on AWS (S3, Glue or similar)
• Workflow orchestration using Airflow and/or Control-M
• Experience with dbt or similar transformation frameworks
• Exposure to distributed query engines (e.g. Presto / Starburst)
• Solid understanding of data warehousing and lakehouse architectures
• Experience working on large-scale data migration or platform modernisation initiatives

Nice to Have

• Experience in credit risk, financial services, or regulated environments
• Exposure to Redshift or similar MPP databases
• Familiarity with data governance and lineage tooling
• Experience working within medallion or lakehouse architectures

Why Join

• High-impact role driving a strategic credit risk platform transformation
• Opportunity to shape a modern AWS data ecosystem from the ground up
• Work within a large-scale, enterprise data platform uplift program
• Strong engineering culture with a focus on scalability, performance, and best practice