About the role

Our client is a major Australian media and publishing organisation investing heavily in data and AI to power audience insight, personalisation and content intelligence. We are looking for an AWS Data Engineer with hands-on Amazon SageMaker experience to build reliable data pipelines and productionise machine learning workloads on AWS.

You will work alongside data scientists, analysts and platform engineers, owning the path from raw data to features, trained models and monitored production endpoints.

Key responsibilities

- Design, build and maintain scalable batch and streaming data pipelines on AWS (Glue, EMR, Lambda, Kinesis/MSK, Step Functions, MWAA/Airflow).
- Develop and optimise data lake and warehouse solutions using S3, Lake Formation, Athena, Redshift and the Glue Data Catalog.
- Prepare and manage ML-ready datasets and features using SageMaker Processing, Data Wrangler and Feature Store.
- Build and operate ML pipelines with SageMaker Pipelines, including training, tuning, evaluation and Model Registry workflows.
- Deploy and support models via real-time, batch transform, serverless and asynchronous endpoints; implement monitoring for drift, data quality and performance (Model Monitor, CloudWatch).
- Implement data quality, lineage, governance and access controls in line with security and privacy requirements.
- Automate infrastructure and deployments using IaC and CI/CD (Terraform, CloudFormation or CDK; Git-based pipelines).
- Optimise cost,



performance and reliability across data and ML workloads.
- Collaborate with data science, product and engineering teams; document solutions and share knowledge.

Essential skills and experience

- 4+ years in data engineering, with strong delivery experience on AWS.
- Proven hands-on experience with Amazon SageMaker (Pipelines, Processing, Feature Store, training jobs, endpoints).
- Robust Python and SQL; experience with PySpark or similar distributed processing.
- Solid experience with core AWS data services: S3, Glue, Athena, Redshift, Lambda, IAM, KMS.
- Experience with workflow orchestration (Airflow/MWAA or Step Functions).
- Infrastructure as code and CI/CD experience (Terraform, CloudFormation or CDK).
- Good understanding of data modelling, partitioning and columnar formats (Parquet, Iceberg or Delta).
- Strong communication skills and ability to work in agile, cross-functional teams.

Desirable

- AWS certifications (Data Engineer – Associate, Machine Learning Engineer – Associate, ML – Specialty, Solutions Architect).
- Streaming experience (Kinesis, MSK/Kafka, Spark Streaming).
- Exposure to MLOps practices: model versioning, A/B testing, automated retraining, monitoring.
- Experience with recommendation, personalisation, NLP or content-related ML use cases.
- Familiarity with Docker, containers and data observability tooling.
- Background in media, publishing, digital or subscription businesses.

📌 AWS Data Engineer (SageMaker) (Sydney)
🏢 Dipole Group
📍 Sydney