Responsibilities
Building and maintaining dbt transformation models across multiple pipeline layers — from raw ingestion through to governed, consumer-ready datasets
Writing Snowflake SQL for complex transformations: type casting, deduplication, incremental load logic, surrogate key generation
Developing and debugging Apache Airflow DAGs on AWS ECS
Contributing to AWS Lambda-based ingestion( Python) — file validation, control file generation, S3 event handling
Maintaining source-to-target mapping documentation and aligning models to business requirements and data specs
Supporting onboarding of downstream consumers via data sharing mechanisms and event-driven notifications
Participating in code reviews, writing YAML-based data quality tests,



and owning tickets from design through to production
Tech stack required
Solid SQL
Proficiency with dbt Core (Must have)
Apache Iceberg Table format (Nice to have)
Apache Airflow
AWS -
Working Knowledge of S3, Lambda, MWAA, ECS, Glue Catalogs, Lake Formation, Lambda, SQS, EventBridge
Experience with fixed-format or structured financial data files
CICD with Github Actions.
Confidentiality is assured, and only shortlisted candidates will be notified for interviews
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📌 Staff Data Engineer (Sydney)
🏢 Recruit CRM
📍 Sydney