About the role

The Senior AWS Data Engineer is responsible for designing, building, and supporting scalable data pipelines and curated datasets on AWS. You will work with cross-functional teams to ingest, transform, and serve data for reporting, analytics, and downstream applications. The ideal candidate is hands-on, solid in SQL/Python, and experienced with AWS-native data services and contemporary data engineering practices.

Key responsibilities
Design, develop, and maintain end-to-end data pipelines (batch and near real-time) on AWS Data Platform
Build and manage ETL/ELT workflows using AWS services (e.g., AWS Glue, S3, Redshift, Athena, EMR), dbt and orchestration tools such as Airflow
Implement data ingestion patterns from diverse sources (databases, APIs, files, event streams) into lake/warehouse layers such as raw, cleansed, and curated data layers
Develop transformation logic using SQL and Python/PySpark for cleansing, enrichment, and standardisation
Implement robust data quality checks, reconciliation controls, and monitoring/alerting for failures and anomalies
Collaborate with data analysts/data scientists to model datasets for analytics and machine learning consumption
Contribute to DataOps/DevOps practices: version control, CI/CD, automated testing, release management,



and operational support
Produce and maintain technical documentation (data flows, mappings, job schedules, runbooks, and operational procedures)
Optimise Data Pipeline performance and support workflow orchestration and scheduling
Support production deployments and operations

About you
8–10 years' experience as a Data Engineer
Advanced SQL skills
Hands-on experience working with Teradata and Siebel CRM data sets
Experience delivering data pipelines in a large-scale enterprise data platform environment
Strong hands-on AWS experience with common data services such as: Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR and dbt
Solid programming capability in Python and solid data transformation experience using PySpark (preferred) and/or Spark
Experience with workflow orchestration tools such as Airflow
Solid understanding of data warehousing concepts (dimensional modelling, partitioning, incremental loads, CDC concepts)
Experience implementing monitoring, logging, alerting, and operational support processes
Strong communication skills and ability to work with stakeholders to translate requirements into data deliverables

📌 Aws Data Engineer/data Sme Sydney (Australia)
🏢 AUSDX
📍 Australia