Location: Sydney, NSW
Experience: 9–14 Years
Employment Type: Full-Time
Eligibility: Australian Citizens & Permanent Residents (PR) Only
Sponsorship: Not Available

About the Role

We are seeking an experienced Data Engineer with expertise in AWS SageMaker Unified Studio to design and build scalable data platforms, real-time and batch data pipelines, and enterprise data solutions on AWS.

Key Responsibilities

• Design and develop batch and real-time data ingestion frameworks.
• Build ETL/ELT pipelines using Python, PySpark, SQL, and Apache Airflow (MWAA).
• Develop streaming solutions using AWS Kinesis, Kafka, or similar technologies.
• Build and manage solutions in AWS SageMaker Unified Studio, including ML pipelines, data processing, governance, and project setup.
• Develop data solutions using Amazon Redshift, S3, Glue, Lambda, and DynamoDB.
• Manage Airflow workflows, monitoring, logging, and performance optimisation.
• Collaborate with Data Engineers, Data Scientists, and Architects to deliver scalable AWS data solutions.

Required Skills

• 9–14 years of Data Engineering experience.
• Strong hands-on experience with:
  • AWS SageMaker Unified Studio
  • AWS
  • Python
  • PySpark
  • SQL
  • Apache Airflow (MWAA)
  • Amazon Redshift
• Experience with AWS Kinesis, Apache Kafka, or similar streaming platforms.
• Strong knowledge of AWS services including S3, Glue, Lambda, DynamoDB, CloudWatch, SQS, SNS, API Gateway, and IAM.
• Experience with relational and NoSQL databases (Oracle, RDS, Redshift, MongoDB, Snowflake, DynamoDB).
• Good understanding of Data Warehousing, Data Lakehouse, Git, CI/CD, Docker, and Infrastructure as Code (Terraform/CloudFormation).

Preferred Skills

• Experience with Machine Learning pipelines, MLOps, and Generative AI in SageMaker.
• Data Modelling experience.
• Enterprise-scale AWS implementation experience.

Eligibility

• Australian Citizens & Permanent Residents (PR) only.
• Candidates must be currently residing in Australia.
• No visa sponsorship is available.