Senior Cloud Data Engineer

Nucleome Therapeutics Ltd

Oxford, UK

9 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Compensation

£107,761.0

Working hours

Regular working hours

Job source

www.adzuna.co.uk

Tech stack

Airflow Amazon Web Services Amazon S3 Big Data Cloud Computing Cloud Database Data as a Services DevOps Identity and Access Management Python (Programming Language) PostgreSQL Software Architecture

+16 more

Data Logging Software Repository Pulumi Delivery Pipeline Amazon Virtual Private Cloud (VPC) Amazon Relational Database Service Kubernetes Infrastructure Automation Frameworks Information Technology AWS Fargate Non-relational Database Search Engines Data Management Terraform Data Pipelines Dynatrace

Job description

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).

Requirements

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.