Hybrid Cloud Big Data Engineer - DataOps & Ingest

National University of Singapore; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

The Cancer Science Institute of Singapore, part of the National University of Singapore, seeks a Big Data Engineer for the Genomics and Data Analytics Core (GeDaC). You will join a specialist team, handling data logistics, infrastructure, and scale in a petabyte-scale Data Nexus that underpins production AI for cancer research.

You will focus on data ingestion, IaC deployment (AWS), data governance, and hybrid cloud synchronization, ensuring reliable, scalable data pipelines and fast, queryable

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• 2+ years of Data Engineering/DevOps experience.
• Experience with commercial cloud infrastructure (AWS preferred).

Responsibilities

• Architect and maintain data ingestion pipelines and data logistics from sequencing instruments to hybrid storage.
• Deploy and manage AWS resources using IaC (CloudFormation/Terraform) with DevSecOps practices.
• Implement data governance, provenance, immutable audit logs, and automated access control policies.
• Manage data synchronization between on-prem HPC and AWS storage tiers.
• Coordinate with pipeline teams to stage data for Nextflow/Kubernetes processing.
• Maintain metadata databases with low-latency access for the API layer.

Skills

Python

IaC

SQL/Datastore

Linux/Unix

Cloud/AWS

Education

Bachelor’s degree in CS/IS/Engineering

Tools

AWS CloudFormation

Terraform

Nextflow

Kubernetes

Job description

The Cancer Science Institute of Singapore, part of the National University of Singapore, seeks a Big Data Engineer for the Genomics and Data Analytics Core (GeDaC). You will join a specialist team, handling data logistics, infrastructure, and scale in a petabyte-scale Data Nexus that underpins production AI for cancer research.

You will focus on data ingestion, IaC deployment (AWS), data governance, and hybrid cloud synchronization, ensuring reliable, scalable data pipelines and fast, queryable