1 to 25 of 78 GCP Jobs in Yorkshire

tooling: Cursor, Windsurf, GitHub copilot • AI powered platform development: Lovable, Replit, Bolt • APIs & Microservices: RESTful APIs, GraphQL, microservice architectures • Cloud & DevOps: AWS, Azure, or GCP; Docker; CI/CD pipelines • Data Integration: Products integrating ML/AI models, databases, BI tools • UI/UX Awareness: Collaborate on interfaces, data visualisation ...

proficiency in one or more programming languages (e.g., Python, Java, JavaScript) and experience with software architecture patterns. Experience with cloud platforms (e.g., AWS, Azure, GCP) and infrastructure as code. Familiarity with CI/CD pipelines, automated testing, and modern software development practices. Knowledge of technologies such as microservices, containerisation (Docker ...

multiple programming languages such as Python, JavaScript, or similar. Extensive experience writing well-tested code. Good knowledge of cloud platforms (e.g., AWS, Azure, GCP) and infrastructure as code Experience with CI/CD pipelines and automated testing frameworks. Experience with various architecture patterns eg microservices and event-driven. Experience with ...

ingestion and transformation, and event-driven (Pub/Sub) architectures . This role focuses on building secure, scalable identity and access workflows across Google Cloud Platform, integrating identity services with custom APIs, Pub/Sub pipelines, and cloud-native applications. You will collaborate closely with tech leads, Cloud Security, DevOps ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

Solid experience with RESTful APIs, microservices, authentication/authorisation, and database design (SQL/NoSQL). Strong understanding of cloud platforms (AWS, Azure, or GCP). Experience mentoring junior developers or leading small development teams. Familiarity with AI/ML frameworks, data pipelines, or integrating large language models/APIs. ...

Solid experience with RESTful APIs, microservices, authentication/authorisation, and database design (SQL/NoSQL). Strong understanding of cloud platforms (AWS, Azure, or GCP). Experience mentoring junior developers or leading small development teams. Familiarity with AI/ML frameworks, data pipelines, or integrating large language models/APIs. ...

Solid experience with RESTful APIs, microservices, authentication/authorisation, and database design (SQL/NoSQL). Strong understanding of cloud platforms (AWS, Azure, or GCP). Experience mentoring junior developers or leading small development teams. Familiarity with AI/ML frameworks, data pipelines, or integrating large language models/APIs. ...

Solid experience with RESTful APIs, microservices, authentication/authorisation, and database design (SQL/NoSQL). Strong understanding of cloud platforms (AWS, Azure, or GCP). Experience mentoring junior developers or leading small development teams. Familiarity with AI/ML frameworks, data pipelines, or integrating large language models/APIs. ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...

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 ...

modern DevOps tooling within engineering teams. Cloud/DevOps Engineer Requirements Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Strong understanding of cloud-native architectures and modern technical stacks. Proficiency with Infrastructure as Code, particularly Terraform. Experience with CI/CD tooling ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

inputs What you'll bring Strong experience in data engineering, analytics engineering, or applied data science Cloud and data platform exposure, AWS, Azure, GCP, and/or Databricks Python, ideally PySpark, R helpful but not essential End-to-end pipelines, feature engineering, and production ML workflows Familiarity with LLMs, GenAI ...

inputs What you'll bring Strong experience in data engineering, analytics engineering, or applied data science Cloud and data platform exposure, AWS, Azure, GCP, and/or Databricks Python, ideally PySpark, R helpful but not essential End-to-end pipelines, feature engineering, and production ML workflows Familiarity with LLMs, GenAI ...

inputs What you'll bring Strong experience in data engineering, analytics engineering, or applied data science Cloud and data platform exposure, AWS, Azure, GCP, and/or Databricks Python, ideally PySpark, R helpful but not essential End-to-end pipelines, feature engineering, and production ML workflows Familiarity with LLMs, GenAI ...