Platform Data Engineer
Posted
Summary
Data engineer in Singapore building production data infrastructure for disease surveillance at PathGen. Day to day: building and monitoring scalable ingestion pipelines for genomic, scientific, climatic and socio-economic data, automating ETL/ML workflows, maintaining graph databases, and deploying ML models via FastAPI on AWS/K8s. Core stack: Python, Kafka, Airflow, Docker, Terraform, GitHub Acti
We are looking for a pragmatic and technically skilled Data Engineer with a focus on engineering for diverse data types, including natural language, geospatial, and biological data. This role is ideal for someone with experience in building robust data pipelines to support health or biological applications.
This role works across leading global institutions, government agencies, and regional partners at the intersection of AI, public health, and pathogen genomics to build production data infrastructure for disease surveillance.
Responsibilities
Build and monitor scalable data ingestion pipelines for structured datasets (e.g., genomic repositories, scientific literature, climatic datasets, socio-economics).
Automate ETL processes, pre-processing workflows, and model lifecycle operations (Kafka, Airflow).
Expand and refine our graph database of public-health knowledge.
Collaborate with bioinformatics experts to ensure data quality and traceability.
Assist with deployment and maintenance of machine learning models using FastAPI and containerized microservices.
Manage model inference endpoints and batch inference workflows (e.g., AWS Batch or K8s).
Implement CI/CD pipelines for ML workflows using tools like GitHub Actions, Docker, and Terraform.
Essential Qualifications
2+ years of experience deploying and maintaining data pipelines in Python.
Experience with graph databases and diverse datasets.
Strong Python and data skills.
Experience with Docker, container orchestration, and cloud deployments (AWS preferred).
Familiarity with data formats common in genomics and public health (FASTA, GenBank, CSV, JSON).
Experience with data engineering workflows (e.g., Airflow, Prefect, Kafka, K8s).
Strong experience with data engineering best practices.
Bonus Qualifications Experience or familiarity with one or more of the following areas will be highly advantageous:
Domain expertise or strong interest in genomics, biology, or public health.
Federated Learning frameworks and privacy-preserving technologies.
Blockchain technologies, smart contracts, or distributed ledgers.
Desired Personal Attributes
Start-up mentality (flexible & pragmatic)
Strong collaboration skills in multi-disciplinary environments.
Systems thinking and a bias toward reliability & scalability
Ability to design practical, maintainable data & ML infrastructure
What We Offer
A mission-driven environment focused on improving global public health.
Opportunities to work on high-impact problems in AI and pathogen genomics.
A flexible and collaborative work culture with a commitment to innovation.
Low bureaucracy with direct ownership of the product — no ticket-factory work.
Competitive salary and paid leave.
Hybrid-friendly work pattern.
Join us to help operationalize the future of genomic intelligence and deploy real-world AI tools that matter.
This role works across leading global institutions, government agencies, and regional partners at the intersection of AI, public health, and pathogen genomics to build production data infrastructure for disease surveillance.
Responsibilities
Build and monitor scalable data ingestion pipelines for structured datasets (e.g., genomic repositories, scientific literature, climatic datasets, socio-economics).
Automate ETL processes, pre-processing workflows, and model lifecycle operations (Kafka, Airflow).
Expand and refine our graph database of public-health knowledge.
Collaborate with bioinformatics experts to ensure data quality and traceability.
Assist with deployment and maintenance of machine learning models using FastAPI and containerized microservices.
Manage model inference endpoints and batch inference workflows (e.g., AWS Batch or K8s).
Implement CI/CD pipelines for ML workflows using tools like GitHub Actions, Docker, and Terraform.
Essential Qualifications
2+ years of experience deploying and maintaining data pipelines in Python.
Experience with graph databases and diverse datasets.
Strong Python and data skills.
Experience with Docker, container orchestration, and cloud deployments (AWS preferred).
Familiarity with data formats common in genomics and public health (FASTA, GenBank, CSV, JSON).
Experience with data engineering workflows (e.g., Airflow, Prefect, Kafka, K8s).
Strong experience with data engineering best practices.
Bonus Qualifications Experience or familiarity with one or more of the following areas will be highly advantageous:
Domain expertise or strong interest in genomics, biology, or public health.
Federated Learning frameworks and privacy-preserving technologies.
Blockchain technologies, smart contracts, or distributed ledgers.
Desired Personal Attributes
Start-up mentality (flexible & pragmatic)
Strong collaboration skills in multi-disciplinary environments.
Systems thinking and a bias toward reliability & scalability
Ability to design practical, maintainable data & ML infrastructure
What We Offer
A mission-driven environment focused on improving global public health.
Opportunities to work on high-impact problems in AI and pathogen genomics.
A flexible and collaborative work culture with a commitment to innovation.
Low bureaucracy with direct ownership of the product — no ticket-factory work.
Competitive salary and paid leave.
Hybrid-friendly work pattern.
Join us to help operationalize the future of genomic intelligence and deploy real-world AI tools that matter.