Mid-Level Platform Engineer, Biotech

About the Role

Join the platform and data engineering team at an AI-focused biotech startup, building and operating infrastructure for sensitive clinical and biomedical data. Your work will help scientists and machine learning engineers access reliable, model-ready datasets and deploy data and models into partner environments.

What You'll Do

• Build and maintain cloud data infrastructure, including Kubernetes clusters, object storage, lakehouse layouts, catalogs, lineage, versioning, and data access systems.
• Design and operate data warehouses and lifecycle processes for clinical records, molecular features, metadata, and versioned dataset releases.
• Develop reproducible bioinformatics workflows for omics data, using workflow managers such as Nextflow and containerized cloud compute.
• Build data quality checks, biological validation, and visualizations that help teams identify issues throughout processing.
• Create reliable, versioned interfaces for preparing model-training datasets and delivering data alongside models to partner environments.
• Own infrastructure reliability, observability, security, compliance controls, and cost optimization, including CI/CD and workload orchestration.

What We're Looking For

• Two to five years building data infrastructure and production pipelines, with experience in biotech, diagnostics, biobanking, or health data involving sensitive or biological information.
• Production experience building and operating Kubernetes clusters, writing infrastructure as code with Terraform or similar tools, and designing CI/CD and deployment systems.
• Strong production programming skills in Python and/or Go, with clean, tested, well-documented code and effective code review practices.
• Experience with a major cloud provider, preferably AWS, distributed job orchestration, and large-scale data pipelines.
• Familiarity with lakehouse and warehouse design, data catalogs, Docker, and workflow managers such as Nextflow.
• Experience with GPU or machine learning infrastructure, distributed training, omics workflows, or clinical data standards such as OMOP and FHIR is valuable.
• Knowledge of controls and audit practices for HIPAA, GDPR, or SOC 2 is beneficial; R or SQL experience is also useful.