Senior Data Engineer — MLOps & AWS Pipelines

Great Eastern; Kuala Lumpur; Hybrid; MYR 120,000 - 180,000; Full time

Job summary

Great Eastern is seeking a hybrid Data Engineer to design and operate data pipelines, ML lifecycle automation, and scalable production infrastructure in AWS. You will translate data science experiments into reliable, production-grade applications while maintaining robust ETL processes.

The role emphasizes end-to-end MLOps on AWS, containerization, CloudFormation, security governance, and collaboration with data science teams.

Qualifications

• Experience designing end-to-end MLOps pipelines on AWS.
• Strong debugging and problem-solving in cloud environments.
• Background in DevOps or infrastructure engineering.
• Scripting or automation with Python, YAML, JSON is a plus.
• Good communication and teamwork skills.

Responsibilities

• Design and implement end-to-end MLOps pipelines (CI/CD/CT) on AWS.
• Containerize applications and build orchestration environments.
• Maintain robust ETL pipelines for data ingestion and storage.
• Develop and manage AWS CloudFormation templates.
• Administer AWS platforms, ensuring security, availability, scalability.
• Configure and manage AWS services including IAM, VPC, Lambda, SageMaker, S3, CloudWatch.
• Collaborate with data science teams on cloud deployment and access.
• Monitor platform health, logs, metrics, and alerts; troubleshoot issues.
• Enforce cloud security, governance, and compliance best practices.
• Keep abreast of regulatory compliance and industry threats.
• Highlight potential concerns and share risk management practices.

Skills

Cloud security

Networking

Monitoring

Operational support

CI/CD pipelines

Troubleshooting

Python scripting

Communication

Cross-functional collaboration

Tools

AWS core services

CloudFormation

SageMaker

IAM

VPC

Lambda

S3

CloudWatch

DataSync

Job description

Great Eastern is seeking a hybrid Data Engineer to design and operate data pipelines, ML lifecycle automation, and scalable production infrastructure in AWS. You will translate data science experiments into reliable, production-grade applications while maintaining robust ETL processes.

The role emphasizes end-to-end MLOps on AWS, containerization, CloudFormation, security governance, and collaboration with data science teams.