Platform Engineer, Biotech

Job summary

Build and operate secure, governed data infrastructure that transforms clinical, genomic, imaging, and other biomedical data into reliable, model-ready assets for scientific and machine learning teams. The role covers cloud data platforms and warehouses, reproducible omics workflows, data quality and biological validation, and versioned training datasets. It also includes production operations across Kubernetes, CI/CD, cloud batch compute, security, reliability, observability, and cost optimization.

Qualifications

• Two to five years of experience building data infrastructure and production pipelines as a platform engineer in biotech, diagnostics, biobanking, health data, or a related field involving sensitive biological or clinical data.
• Production experience operating Kubernetes clusters.
• Experience writing infrastructure as code with Terraform or similar tools.
• Experience shipping tested, documented Python and/or Go code.
• Experience designing CI/CD and deployment systems and orchestrating distributed workloads.
• Familiarity with a major cloud provider, preferably AWS, Docker, workflow managers such as Nextflow, lakehouse and warehouse design, data catalogs, and orchestration.
• Experience with large-scale data pipelines, machine learning infrastructure, or GPU workloads is valuable.
• Knowledge of omics workflows, bioinformatics tools, OMOP, FHIR, R, or SQL is relevant.
• Experience implementing compliance controls and audit evidence for HIPAA, GDPR, or SOC 2 is beneficial.

Responsibilities

• Build and maintain cloud data platform components, including object storage, lakehouse architecture, catalogs, lineage, dataset versioning, and query and access layers.
• Design and operate data warehouses and governed systems for clinical records, molecular features, metadata, retention, and dataset releases.
• Develop reproducible omics workflows using containerized pipelines and cloud batch compute.
• Implement data quality checks, biological validation, and visualizations across processing steps.
• Deliver versioned, training-ready datasets and data deployment interfaces for partner environments.
• Operate production infrastructure, including Kubernetes, CI/CD, workload orchestration, security, reliability, observability, and cost optimization.

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