• 10+ years building and operating production data infrastructure, with a track record of architecting and owning platforms end to end.
• Deep AWS expertise: S3, IAM, EC2, Batch, Lambda, and Spark (EMR or equivalent). Experience as technical owner of production cloud environments, including multi-account architecture and cross-account access.
• Strong Python and SQL, with a background in production-grade pipeline development.
• Production experience with workflow orchestration (Nextflow, Airflow, Temporal, Step Functions, Redun, or similar) and the judgement to evaluate and select among them.
• Infrastructure as code (CloudFormation/CDK), CI/CD, containers, and observability. DevOps is a core competency for this role.
• Data modelling and warehouse or lakehouse design, including metadata management, lineage, and data quality practice.
• Cost governance at scale, including budgets, tagging, rightsizing, and storage lifecycle across hundreds of terabytes.
• Fluency with scientific data semantics (studies, samples, donors, cell lines, assays) and the ability to partner directly with scientists to translate their questions into data models.
• Familiarity with genetics and genomics data.
• Microscopy or high-content screening data, including large binary object stores, transfer optimization, and image streaming for model training.
• Databricks, OpenMetadata, or a comparable catalog in production.
• Snowflake, particularly governed access patterns over cloud object storage.
• ML infrastructure: SageMaker or Databricks ML, MLflow or Weights & Biases, feature stores.
• Serving data to LLM or agent clients (MCP servers, tool APIs).
• Experience leading data platform architecture as a senior or principal individual contributor, ideally in biotech, pharma R&D, or a research institute.