16 of 16 Amazon Kinesis Jobs in the UK excluding London

Data Factory. Azure Data Engineer Associate and Databricks Certified Data Engineer Professional AWS Proficiency with AWS Tools: Demonstrable experience using AWS Glue, AWS Lambda, Amazon Kinesis, Amazon EMR , Amazon Athena, Amazon DynamoDB, Amazon Cloudwatch, Amazon SNS and AWS Step Functions. Programming Skills: Strong ...

data pipelines using Java (primary) and technologies like Kafka, Flink, or Spark Developing real-time streaming solutions and event-driven architectures using AWS services (Kinesis, MSK, Lambda, Glue) Contributing to data architecture, modelling, and best practices across modern data platforms Deploying and maintaining solutions using CI/ ...

data pipelines using Java (primary) and technologies like Kafka, Flink, or Spark Developing real-time streaming solutions and event-driven architectures using AWS services (Kinesis, MSK, Lambda, Glue) Contributing to data architecture, modelling, and best practices across modern data platforms Deploying and maintaining solutions using CI/ ...

data governance, quality, lineage, and security practices. Experience with real-time/streaming data technologies, data Ingestion/ETL (e.g., Apache Kafka, Apache NiFi, Kinesis, Pub/Sub). Experience in, or knowledge of, DevSecOps Tooling and Processes. Self-starter with the ability to appropriately prioritise and plan complex ...

least one cloud data platform (e.g. Databricks, Snowflake, AWS, Azure or GCP) and experience of associated technologies (for example with AWS: DynamoDB, Redshift, Kinesis Pipelines, Glue, Lambda, QuickSight, etc) ideally including practical/hands-on experience of migration to or adoption of a cloud data platform. Strong Databricks expertise ...

hands-on experience of at least one cloud platform (AWS, Azure or GCP) and experience of associated technologies (for example with AWS: DynamoDB, Redshift, Kinesis Pipelines, Glue, Lambda, QuickSight, etc) Hands-on dataops experience (eg. CI/CD, version control, IaC, etc.) Have experience adopting a Data Product mindset ...

connecting enterprise API integrations, and implementing agentic guardrails. Data Engineering (30%): Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application ...

connecting enterprise API integrations, and implementing agentic guardrails. Data Engineering (30%): Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application ...

connecting enterprise API integrations, and implementing agentic guardrails. Data Engineering (30%): Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application ...

management. Experience in developing data analytics and AI-driven solutions. Skilled in designing and automating data quality metrics and KPIs. Proficient with AWS (S3, Kinesis, Glue, Redshift, Lambda, EMR) and/or Azure (ADF, Synapse, Fabric, Functions). To be Considered.... Please either apply by clicking online or emailing ...

closely with engineers across the platform. Essential Skills & Experience Strong commercial experience with Node.js and TypeScript Experience building APIs and microservices Experience with Kafka, Kinesis, or similar streaming technologies Strong understanding of event-driven architectures Hands-on AWS experience Experience with SQL and/or NoSQL databases Familiarity with ...

Shores, we’re creating a world of work where potential matters more than experience. Our market-leading, task-based psychometric assessments help organisations like Amazon, Siemens and Jet2 make fair, evidence-based hiring decisions. We’ve doubled ARR year-on-year for the past three years and are looking … Typescript, Python Http4s, Http4k, Circe, Cats, Cats-Effect, Doobie, React, Exposed Testing Specs2, ScalaCheck Infrastructure & DevOps AWS (EKS, Lambda, RDS/Postgres, DynamoDB, SQS, Kinesis, S3, Cognito, Route53, VPC, EC2) Terraform, Kubernetes, Helm, Ansible, Puppet GitLab CI Observability & Data Prometheus, Grafana, OpenSearch Snowflake You don’t need experience ...

Software Engineering, Agentic & AI Development (30%): Solid software engineer principles (pipelines, toolsets, testing, linting, vulnerability scanning, etc.) Production‐level experience with tools such as Amazon Bedrock, AWS AgentCore, Azure AI Studio, LangChain, and writing automation scripts or custom AI agents using node or python. Data Engineering (30%): Experience deploying … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector ...

Software Engineering, Agentic & AI Development (30%): Solid software engineer principles (pipelines, toolsets, testing, linting, vulnerability scanning, etc.) Production‐level experience with tools such as Amazon Bedrock, AWS AgentCore, Azure AI Studio, LangChain, and writing automation scripts or custom AI agents using node or python. Data Engineering (30%): Experience deploying … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector ...

Software Engineering, Agentic & AI Development (30%): Solid software engineer principles (pipelines, toolsets, testing, linting, vulnerability scanning, etc.) Production‐level experience with tools such as Amazon Bedrock, AWS AgentCore, Azure AI Studio, LangChain, and writing automation scripts or custom AI agents using node or python. Data Engineering (30%): Experience deploying … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector ...

edge environments, using platforms such as AWS or Azure, with a strong understanding of distributed systems and event‐driven architectures (e.g. Apache Kafka, AWS Kinesis, messaging and streaming patterns). Experience designing architectures for high volume, time critical, or sensor driven data, including streaming, time series, and geospatial data … senior leaders. Hands‐on experience with modern cloud native data platforms, such as: MODCloud – ICE, ACE environments AWS data services (e.g. S3, Redshift, EKS, Kinesis) Azure data services (e.g. Data Lake, Synapse, AKS) GCP Kubernetes‐based platforms and containerised data services Telicent Understanding of NATO data standards, interoperability requirements ...