Senior Engineer, Data Infrastructure

About this role

<p><strong>Position Summary:</strong></p> <p>Quotient is seeking a Senior Engineer in our Data Infrastructure team to design and evolve our cloud, data and AI infrastructure underpinning our genomics-first drug discovery platform.</p> <p>Working within the Data Infrastructure team, you will lead complex initiatives spanning AWS platform engineering, scientific data systems and AI/ML workloads. You will collaborate closely with cloud engineers, data engineers, bioinformaticians, AI scientists, computational biologists, and experimental scientists to turn evolving scientific needs into secure, reliable and scalable engineering solutions.</p> <p>This is a senior individual-contributor role with substantial technical autonomy. You will make cross-system architecture and operational trade-offs, establish reusable engineering patterns, and mentor our Cloud Engineers and Data Engineers through design reviews, delivery and troubleshooting. The role does not require formal line management, but it does require clear technical leadership and accountability for platform outcomes.</p> <p>This hybrid role is based at the Chesterford Research Campus, near Cambridge, and reports to the Head of Data Infrastructure. The successful candidate will normally work from the campus at least three days per week, participate in operational support and incident escalation, and must have permission to work in the UK.</p> <p><strong>Responsibilities:&nbsp;</strong></p> <ul> <li>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.</li> <li>Design and implement reusable infrastructure-as-code, CI/CD and environment-management patterns that make production systems easier to build, test, deploy and operate.</li> <li>Own cross-cutting engineering decisions for observability, identity and access management, networking, resilience, capacity, data movement and lifecycle management.</li> <li>Partner with Cloud Engineers and Data Engineers to deliver integrated solutions, providing practical mentoring through technical design, code review, incident response and operational improvement.</li> <li>Work with AI scientists, computational biologists and scientific teams to translate model development, training, evaluation, inference and data requirements into secure, reproducible platform capabilities.</li> <li>Diagnose and resolve complex failures spanning infrastructure, data pipelines, applications and AI workloads; lead incident reviews and ensure corrective actions improve the wider system.</li> <li>Establish and reinforce engineering standards for testing, documentation, release management, operational readiness, security and cost visibility across the Data Infrastructure team.</li> <li>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.</li> </ul> <p><strong>Qualifications:</strong></p> <p><u>Essential</u></p> <ul> <li>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.</li> <li>Advanced hands-on experience with AWS and ownership of multiple interdependent production systems, including the ability to troubleshoot failures across service boundaries.</li> <li>Strong experience with infrastructure-as-code, CI/CD, containerised workloads, cloud networking, identity and access management, and production observability.</li> <li>Strong software engineering skills in Python and Linux, including automated testing, version control, code review and maintainable automation or platform tooling.</li> <li>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.</li> <li>Experience enabling AI/ML workloads in cloud environments, such as data preparation, model training, evaluation, inference, orchestration or production deployment.</li> <li>A track record of leading complex technical initiatives under ambiguity, making explicit trade-offs and remaining accountable for reliable operational outcomes.</li> <li>Demonstrated ability to mentor engineers and communicate architecture, risk and delivery choices clearly to both technical and scientific stakeholders.</li> </ul> <p><u>Desirable Experience</u></p> <ul> <li>Genomics, digital pathology, structural biology or other high-volume scientific data modalities and formats.</li> <li>AWS CDK, Batch, ECS, EKS, S3, RDS or Postgres, Athena, SageMaker, Bedrock, or equivalent services.</li> <li>Scientific workflow and data orchestration frameworks such as Nextflow, Dagster, Airflow or similar.</li> <li>Platform security, privacy or governance controls for sensitive biomedical or clinical data.</li> <li>Identity platforms and federation technologies such as Entra ID, SSO or role-based access patterns.</li> <li>Working in an early-stage biotech, research-intensive startup or another fast-changing environment with a small engineering team.</li> </ul> <p><strong>Values and Behaviors:</strong></p> <ul> <li>Encourage respectful disagreement and cultivate open-minded, ego-free interactions to continuously push each other towards excellence</li> <li>Seek out diverse perspectives; practice active listening and genuine curiosity to ensure all contributions are valued, regardless of source</li> <li>Recognize the impact of your behavior, language and attitudes, and strive for balanced, meaningful exchanges that enhance mutual growth and understanding in all interactions</li> <li>Use a company-first mindset to guide decision-making; prioritize team over individual success</li> <li>Take calculated risks and challenge convention in the quest for exceptional