About the role
Overview of the role
We are looking for an experienced Cloud Data Engineer to join our expanding computational team.
Working in collaboration with engineering, bioinformatics, and drug discovery colleagues, you will design,
build, and operate the cloud infrastructure that underpins our genomics data platform; from orchestration
and compute through to storage, search, and machine learning. Your work will focus on scaling this
platform reliably as data volumes and pipeline complexity grow, and on maturing our observability,
security, networking and engineering operational practices so the team can move fast with confidence.
Key responsibilities
Drive the design and evolution of our AWS-based platform. Including EKS cluster capacity, networking
and ingress, and the data services the platform depends on: RDS PostgreSQL, S3 data Lakehouse,
search engines, and SageMaker. You will identify bottlenecks, propose architectural improvements, and
implement changes to keep pipelines and services performant and cost-effective as demand increases.
You will identify opportunities to scale existing and develop new data pipelines which the lab team rely
on to generate the critical data needed for analysis on drug target viability, making your work directly
impactful to therapeutic development priorities.
Own and extend our Pulumi based infrastructure-as-code repositories. You will work closely with the
engineering team on technical and architectural decisions, taking ownership of implementation while
collaborating on strategy, standards, and review. Together you will iterate on platform design and
ensure consistent, repeatable deployments across environments.
Build on our existing observability stack (monitoring, logging, tracing) to deliver deeper visibility into
pipeline health, service performance, and infrastructure utilisation. You will design and implement
alerting and dashboards, extend instrumentation across Dagster jobs, Kubernetes workloads, and AWS
services and introduce distributed tracing where it adds clear operational value. You will also contribute
to cost visibility and optimisation (e.g. compute sizing, spot/on-demand strategy, storage lifecycle).
Required Qualifications and Skills
BSc, MSc or PhD in computer science, engineering, or a related quantitative discipline. Experience in
biotech, pharma, or other data-intensive scientific environments. Solid programming skills (Python
preferred). Experience with CI/CD pipelines, GitOps or similar deployment workflows, and modern
DevOps practices.
Extensive hands-on experience designing and operating production cloud infrastructure on AWS,
including Kubernetes, VPC networking, IAM, S3, RDS, and containerised workloads. Experience with
infrastructure-as-code tools (Pulumi or Terraform etc.) and Helm-based deployments.
Experience scaling data platforms and batch/orchestrated workloads: autoscaling (Karpenter, HPA),
mixed compute strategies (spot, on-demand, Fargate), and performance/cost trade-offs for large-scale
data processing. Experience in data modelling and implementation in relational and non-relational
databases.
Strong practical experience with observability stacks; metrics, logging, and alerting. Familiarity with
distributed tracing and an ability to implement end-to-end observability for data pipelines.
Strong communication, organisational, and time management skills, with the ability to explain complex
infrastructure concepts to both technical and non-technical audiences. Comfortable working
independently and as part of a multidisciplinary team in a fast-moving environment.
Detail oriented in implementation, alongside a clear view of the bigger picture, planning horizons,
operational risk, and business impact.