Healthcare Data Platform Analyst - Research

SingHealth; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

SingHealth is seeking an experienced professional to lead the design and operation of data science platforms and pipelines for research across Singapore's public healthcare cluster.

You will build and maintain data pipelines across clinical, imaging, genomic and administrative datasets, ensuring quality, governance and security. Collaboration with clinicians, researchers and technology teams will be essential to translate needs into scalable data solutions.

Qualifications

• Bachelor's, Master's or PhD in STEM with at least 1.5 years of relevant experience, or non-STEM with at least 4 years.
• Experience in data engineering, data platforms and analytics environments in healthcare is preferred.
• Working knowledge of SQL, Python and/or R with ETL/ELT and data quality management.
• Familiarity with Spark, cloud data platforms and modern data tooling is advantageous.
• Strong problem-solving, analytical skills and collaboration across researchers and technology teams.

Responsibilities

• Design, develop and maintain data pipelines across diverse datasets (clinical, registry, imaging, genomic).
• Validate data quality, standardise controls and ensure governance compliance.
• Operate enterprise data platforms, provision datasets and monitor service stability and security.
• Curate metadata, maintain data dictionaries and improve data discoverability.
• Implement access controls, de-identification and secure data linkage with audit trails.

Skills

SQL

Python

ETL/ELT

Data modelling

CI/CD

Apache Spark

Cloud platforms

Data governance

Problem solving

Education

STEM degree (Bachelor/Master/PhD)

Non-STEM degree + 4+ years experience

Tools

APIs

Docker

CI/CD tooling

Job description

SingHealth is seeking an experienced professional to lead the design and operation of data science platforms and pipelines for research across Singapore's public healthcare cluster.

You will build and maintain data pipelines across clinical, imaging, genomic and administrative datasets, ensuring quality, governance and security. Collaboration with clinicians, researchers and technology teams will be essential to translate needs into scalable data solutions.