AWS Data Engineer/Data SME
AUSDX PTY LTD · Sydney NSW · hybrid
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, strong in SQL/Python, and experienced with AWS-native data services and modern 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
- Strong programming capability in Python and strong 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