Quotient: Senior Engineer, Data Infrastructure

Responsibilities

• Lead the architecture and evolution of our AWS-based infrastructure across cloud services, scientific data platforms and AI/ML workloads, balancing delivery speed, reliability, security and cost.
• Design and implement reusable infrastructure-as-code, CI/CD and environment-management patterns that make production systems easier to build, test, deploy and operate.
• Own cross-cutting engineering decisions for observability, identity and access management, networking, resilience, capacity, data movement and lifecycle management.
• Partner with Cloud Engineers and Data Engineers to deliver integrated solutions, providing practical mentoring through technical design, code review, incident response and operational improvement.
• Work with AI scientists, computational biologists and scientific teams to translate model development, training, evaluation, inference and data requirements into secure, reproducible platform capabilities.
• Diagnose and resolve complex failures spanning infrastructure, data pipelines, applications and AI workloads; lead incident reviews and ensure corrective actions improve the wider system.
• Establish and reinforce engineering standards for testing, documentation, release management, operational readiness, security and cost visibility across the Data Infrastructure team.
• Evaluate new technologies and shape the infrastructure roadmap with the Head of Data Infrastructure, making pragmatic build-versus-buy and sequencing decisions for an early-stage biotech.

Qualifications

• Essential
• Substantial experience designing, building and operating production cloud or data infrastructure, typically gained through seven or more years of relevant work or equivalent demonstrated capability.
• Advanced hands-on experience with AWS and ownership of multiple interdependent production systems, including the ability to troubleshoot failures across service boundaries.
• Strong experience with infrastructure-as-code, CI/CD, containerised workloads, cloud networking, identity and access management, and production observability.
• Strong software engineering skills in Python and Linux, including automated testing, version control, code review and maintainable automation or platform tooling.
• Experience supporting data-intensive life-science systems or workflows, with enough domain understanding to reason about large scientific datasets, reproducibility, provenance and scientific user needs.
• Experience enabling AI/ML workloads in cloud environments, such as data preparation, model training, evaluation, inference, orchestration or production deployment.
• A track record of leading complex technical initiatives under ambiguity, making explicit trade-offs and remaining accountable for reliable operational outcomes.
• Demonstrated ability to mentor engineers and communicate architecture, risk and delivery choices clearly to both technical and scientific stakeholders.
• Desirable Experience
• Genomics, digital pathology, structural biology or other high-volume scientific data modalities and formats.
• AWS CDK, Batch, ECS, EKS, S3, RDS or Postgres, Athena, SageMaker, Bedrock, or equivalent services.
• Scientific workflow and data orchestration frameworks such as Nextflow, Dagster, Airflow or similar.
• Platform security, privacy or governance controls for sensitive biomedical or clinical data.
• Identity platforms and federation technologies such as Entra ID, SSO or role-based access patterns.
• Working in an early-stage biotech, research-intensive startup or another fast-changing environment with a small engineering team.

Values and Behaviors

About Quotient

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