26 to 50 of 907 GCP Jobs in the UK

years of backend or platform engineering experience. Proven experience building and deploying microservices in production within a cloud environment (AWS, Azure, or GCP). Expertise in Go, Node.js, or Java, with deep understanding of distributed systems and API design. Experience integrating or leveraging AI/ML models or services within

across multiple time zones. Advantageous experience Experience with Java and familiarity with the JVM ecosystem; Spring Boot exposure. Cloud experience with AWS, Azure or GCP; infrastructure-as-code (e.g., Terraform, Helm) and secure configuration management. Performance profiling and tuning in .NET (e.g., dotnet-trace, PerfView) and/or JVM environments.

Java and JVM-based systems (3+ years coding experience preferred) Solid background in system design, distributed systems, and cloud architectures (AWS/Azure/GCP) Passion for clean code, testing, and performance optimization Excellent communication, leadership, and decision-making skills A mindset for innovation, problem-solving, and continuous improvement Bonus

frameworks such as React and Express.js (or similar) Strong experience using HTML & CSS Experience with cloud platform providers such as AWS, Azure or GCP (ideally AWS) Experience with CI/CD and DevOps tooling and processes You will be responsible for writing clean and maintainable code that aligns to their

processes. Strong proficiency in SQL and Python. Deep understanding of data modelling, warehousing, and performance optimisation. Proven experience with cloud platforms (AWS, Azure, or GCP) and their data services. Hands-on experience with big data frameworks (e.g. Apache Spark, Hadoop). Strong knowledge of data governance, security, and compliance. Ability

pipelines and infrastructure automation. Required Skills TypeScript/Node.js REST APIs (Express, Nest.js, or similar frameworks) Unit testing (Jest or equivalent) Cloud computing (GCP preferred; AWS or Azure experience also valuable) Infrastructure as Code (Terraform or similar) Docker and Compose Continuous Integration tools and environments Experience in a Continuous Delivery

Swarm. Understanding of containerized environments and their integration into CI/CD pipelines Cloud Platforms: Familiarity with major cloud platforms like AWS, Azure, or GCP; understanding of cloud-specific services and how to integrate them into CI/CD processes Monitoring and Logging: Knowledge of monitoring and logging tools such

systems concepts such as event sourcing, resilience patterns, and asynchronous communication. Experience with containerised development (Docker) and deploying cloud-native applications (Azure, AWS, or GCP). Strong understanding of DevOps concepts: CI/CD pipelines, automated testing, infrastructure-as-code patterns. Proficiency in unit, integration, and end-to-end testing

About You Professional knowledge and experience Essential Proven experience in data engineering, data integration and data modelling Expertise with cloud platforms (e.g. AWS, Azure, GCP) Expertise with modern cloud data platforms (e.g. Microsoft Fabric, Databricks) Expertise with multiple data analytics tools (e.g. Power BI) Deep understanding of data wareho using

should have: Hands-on experience with Kubernetes and Linux-based platform engineering. Strong scripting and automation capability (Python preferred). Familiarity with AWS, Azure, GCP or secure on-prem cloud equivalents. Solid understanding of networking concepts (routing, DNS, firewalls, service mesh). Experience with Elastic for monitoring, logging and observability.

pipeline consuming other data products Basic qualifications: Extensive experience conducting Bankend Development using Python. Concurrency and Asyn exp Demonstrated experience using FastAPI Cloud Experience - GCP Essential SQL Docker Containerization Unit testing experience - TDD or Test Suit ML or AI experience would be desriable. Experience within Life Sceiences or Pharma Sector

roles, with at least 5 years in SRE, Observability, or DevOps functions.Proven track record implementing observability solutions in cloud-native environments (AWS, Azure, or GCP). Hands-on proficiency with observability tools such as Datadog, Grafana, Prometheus, OpenTelemetry. Strong knowledge of distributed systems, microservices, and container orchestration (Kubernetes, Docker).

Kubernetes, and CI/CD platforms Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation and deployment on major cloud platforms (AWS, GCP, or Azure) Comfortable working with ML libraries (e.g. TensorFlow, PyTorch, scikit-learn) and serving frameworks Knowledge of data pipeline orchestration tools such as Airflow

other complex services) into production systems. Experience with containerization (Docker, Kubernetes) and deploying/managing services on a major cloud platform (AWS, GCP, or Azure). This is a permanent position with hybrid working of two days a week in the central London office and the rest WFH. The salary

other complex services) into production systems. Experience with containerization (Docker, Kubernetes) and deploying/managing services on a major cloud platform (AWS, GCP, or Azure). This is a permanent position with hybrid working of two days a week in the central London office and the rest WFH. The salary

Familiarity with BI tools (QuickSight, Looker, PowerBI, Tableau). Exposure to tagging/tracking tools (Snowplow, Tealium). Experience with Terraform & Terragrunt. Knowledge of GCP or Google Analytics is a plus. Our promise to talent At Property Finder, we believe talent thrives in an environment where you can be your

/TypeScript, Java, C# Frameworks: Vue/Angular/React/Node/Next, Spring/Spring Boot, .NET/ASP.NET Cloud platforms: AWS, GCP, Azure CI/CD:Jenkins, GitHub Actions, Azure DevOps IaC: Terraform, CloudFormation, Azure Bicep Pragmatic and safe usage of AI through the SDLC (e.g. using

/TypeScript, Java, C# Frameworks: Vue/Angular/React/Node/Next, Spring/Spring Boot, .NET/ASP.NET Cloud platforms: AWS, GCP, Azure CI/CD:Jenkins, GitHub Actions, Azure DevOps IaC: Terraform, CloudFormation, Azure Bicep Pragmatic and safe usage of AI through the SDLC (e.g. using

passion for quality engineering will be essential. Experience with microservices, DDD, CQRS, advanced Kubernetes, or cloud platforms such as AWS, Azure or GCP will be an advantage, but not required. To be eligible for this role, you must hold or be able to obtain Developed Vetting (DV) clearance, including

level of ability/experience, you will have familiarity, competence or expertise in a subset of these areas: Cloud technologies: AWS primarily Azure and GCP beneficial Cyber security Vulnerability management (e.g. tenable) IDAM (e.g Azure AD, Keycloak) Virtualisation and Containerisation Containerisation (e.g. Docker, Podman) Container management (e.g. Kubernetes) Architectural styles

such as Maven or Gradle Nice to have Experience with React, JavaScript, HTML and CSS Familiarity with cloud platforms such as AWS, Azure or GCP Knowledge of CI/CD tools such as Jenkins or GitHub Actions Experience with microservices and database technologies like PostgreSQL or MySQL This

driven design (DDD). CQRS and Event Sourcing patterns * Kubernetes * Knowledge/Certification in Spring framework. * Experience in cloud technologies AWS/Azure/GCP Please apply for the job if you fit the above criteria.

cloud concepts. Experience contributing to solution design and familiarity with architectural patterns/frameworks. Proven delivery of serverless pipelines in Azure/AWS/GCP (Azure highly desirable). Experience building data transformation pipelines using SQL and NoSQL databases. Hands-on ownership of CI/CD (Jenkins/Azure DevOps

Java or Scala, plus experience with SQL and NoSQL databases Experience with Microsoft SQL Server and SSRS Familiarity with cloud platforms (AWS, Azure or GCP) and containerisation (Docker, Kubernetes) Strong understanding of CI/CD tooling and best practices The Benefits: Salary up to £77,000 Company bonus scheme Hybrid

engineering* Experience designing ML/AI solutions and working with common architecture patterns* Background in serverless data/ML pipelines (Azure preferred, AWS/GCP welcome)* Proficiency in SQL and NoSQL data stores* Broad understanding of machine learning methods (regression, tree-based models, clustering, deep learning, attention models, transformers, vector