51 to 75 of 216 Permanent Amazon-DynamoDB Jobs

standards. Supported by global teams of engineers, QAs, SREs, and DevOps professionals. Comfortable managing a mix of direct reports and shared teams Skills & Experiences AWS Technologies (e.g. ECS, DynamoDB, Lambda, Aurora, SQS, SNS, VPC, Private Link, etc.) Multi-threading & Socket Programming: Java + Spring Applications Architectures - Event Sourcing, CQRS, Polyglot Database, Traditional OLTP, OLAP DevOps - Provisioning-based Infrastructure More ❯

managers from all backgrounds, and experience in the following areas would be a plus: .NET & Cloud Development: .NET Framework/Core (C#, ASP.NET, Web API), AWS (EC2, Lambda, DynamoDB, S3). Infrastructure as Code (IaC): CloudFormation, Terraform, or Serverless. DevOps & CI/CD: TeamCity, Octopus, BitBucket Pipelines, GitHub Actions, Docker, Kubernetes. Software Architecture & Testing: Microservices, TDD, unit testing More ❯

processing tools. -Infrastructure Know-How: Confident working with Terraform and CI/CD pipelines in a cloud-native environment. -Database Familiarity: Skilled in both SQL and NoSQL (PostgreSQL, DynamoDB, OpenSearch, or equivalents), using ORMs like Django or SQLAlchemy. -Observability & Monitoring: Comfortable using tools like CloudWatch, X-Ray, and structured logging to keep systems running smoothly. -Mindset: Curious, Collaborative More ❯

and optimizing backend systems for data processing at scale within AWS. In-depth knowledge and hands-on experience with foundational AWS services, such as ALB, ECS, S3, ElastiCache, DynamoDB, IAM, CloudWatch. Proficiency in both relational and NoSQL databases, with an understanding of database design, optimization, and management. Strong skills in Infrastructure as Code (IaC) tools, such as Terraform More ❯

Microsoft .Net Framework and experience inFront-end JavaScript frameworks like Angular & React - Traditional Relational Database technologies like Oracle, MS SQL Server and - No SQL Databases like MongoDB or DynamoDB - SOA & Microservices architecture implementation using REST APIs, queue-based messaging patterns, exposure toMulesoft/Kong is a plus - On-premise/cloud-basedinfrastructures, SDLC pipelines, and deployments/configurations More ❯

frontends , and a Serverless Lambda backend on AWS . We're using Typescript top to bottom , and Apollo GraphQL sits in the middle. We keep our data in DynamoDB , Postgres and snowflake for our data-lake using one or the other as best suits a particular workload. You can learn more about our tech stack at While we More ❯

Qualifications Experience with scripting and automation tools Familiarity with Infrastructure as Code (IaC) tools such as AWS CDK Knowledge of AWS services such as SQS, SNS, CloudWatch and DynamoDB Understanding of DevOps practices, including CI/CD pipelines and monitoring solutions Understanding of cloud services, serverless architecture, and systems integration Key job responsibilities As a Business Intelligence Engineer … both business and technical teams. Preferred qualifications: Experience with data-specific programming languages/packages such as R or Python Pandas. Experience with AWS solutions such as EC2, DynamoDB, S3, and EMR. Knowledge of machine learning techniques and concepts. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and More ❯

skills. • Strong communication and collaboration skills. Core Skill • Compute: EC2, Lambda, Auto Scaling, Load Balancing • Storage: S3, EBS, EFS • Networking: VPC, Route 53, CloudFront, API Gateway • Databases: RDS, DynamoDB, Aurora • Security & Monitoring: IAM, CloudWatch, CloudTrail, KMS • DevOps & Automation: CloudFormation, Code Pipeline, Elastic Beanstalk • Other Essentials: AWS CLI, Cost Management, Well-Architected Framework Additional Skill • Hands On experience with More ❯

utilizing an array of tools such as: JVM programming languages(Java/Springboot, Groovy, Python), Docker + Kubernetes, Javascript/React, Kafka, Elastic Search, Lucene, Spark, and Cassandra, DynamoDB as well as Neo4J. Obviously very very few folks have touched/been hands-on with all of these tools in production. Thus, the ideal applicant will be a More ❯

Trade Surveillance , which leverages machine learning to detect trading behavior that may trigger regulatory inquiries. Our tech stack includes Java, Python, Apache Spark (on Serverless EMR), AWS Lambda, DynamoDB, S3, SNS/SQS , and other cloud-native services. As part of a high-impact engineering team, you'll help design and implement fault-tolerant data pipelines and services … and optimize Spark jobs on AWS Serverless EMR for processing large-scale time-series datasets Build event-driven and batch processing workflows using Lambda , SNS/SQS , and DynamoDB Read, process, and transform data from a variety of sources: flat files, APIs, and streaming systems Participate in architecture/design discussions, code reviews, and system performance tuning Write … and large-scale data processing Strong programming skills in Java (multithreading, concurrency, performance tuning) Deep experience with Apache Spark and Spark Streaming Proficiency with AWS services , including Lambda, DynamoDB, S3, SNS, SQS, and Serverless EMR Experience with SQL and NoSQL databases Hands-on with Python , especially in data handling (pandas, scikit-learn, etc.) Familiarity with RESTful web services More ❯

and NoSQL databases • Experience with RESTful APIs Preferred Skills/Experience: • Proficient in React, TypeScript, and React Query • Proficient in Python • Experience with AWS including Lambda Functions and DynamoDB • Experience with 508 compliance • Familiarity with DevOps practices • Familiarity with Business Process Models such as Camunda, Flowable, N8n-io Agile experience The role specifically requires candidates with an active More ❯

the ability to design scalable, fault-tolerant applications. Expertise in working with MySQL databases, including design, query optimisation, and performance tuning. Significant experience with AWS services, including S3, DynamoDB, SQS, SNS, IAM, Athena, and CloudWatch. Desirable Skills Familiarity with containerisation technologies (e.g., Docker). Understanding of CI/CD pipelines. Experience with serverless frameworks like Serverless, AWS SAM More ❯

Load Balancing and Enterprise Service Bus frameworks (preferably under Cloud environment) • Agile Scrum and possibly experience with leading a Scrum team as a scrum master or equivalent • PostgreSQL, DynamoDB, HBase, MongoDB, Cassandra • Masters in Computer Science or related field Customer Requirements • Clearance - Ability to obtain and hold a public trust position and favorable suitability based on a CBP More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration More ❯

agentic capabilities using frameworks such as LangChain, CrewAI, AutoGen, and DSPy. AWS Cloud & MLOps: Deploy scalable machine learning workloads on AWS using services like SageMaker, Bedrock, Lambda, S3, DynamoDB, ECS, and EKS. End-to-End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration More ❯

engineering . You should have: ? Deep experience in .NET development (C#, ASP.NET, .NET Core). ? AWS cloud expertise hands-on with services like Lambda, ECS, API Gateway, RDS, DynamoDB, S3, IAM, and more. ? Strong SQL and NoSQL database experience. ? A background in event-driven architectures (Kafka, SNS, SQS). ? Hands-on experience with containerisation (Docker, Kubernetes) . ? Strong More ❯

Reactive Programming and Distributed Computing to build these services, using Scala, Akka, Kafka, Play and Cats, as well a wide range of cloud-native technologies including AWS (Kinesis, DynamoDB, Lambda), Docker and Serverless. We have a mature DevOps culture in place, where the team is responsible for the infrastructure and deployment of those applications - "You build it, you More ❯

of libraries such as the Django framework. We stand by a serverless first approach and thus take advantage of the multitude of services from AWS such as Lambda, DynamoDB, RDS, AppSync, and many more to complete our tech stack. We are proud of our tech stack and with the help and guidance of all our software engineers, we More ❯

candidates who thrive in inclusive, growth-oriented environments. Our tech stack: We use technologies that support reliability, scale, and rapid iteration: Backend: Python-based microservices Cloud: AWS Databases: DynamoDB, PostgreSQL Infrastructure: Terraform DevOps: Docker, CircleCI, GitHub Your day-to-day In the ITE Team we deeply embrace the DevOps culture, as we design, build, test, deploy and monitor More ❯

Dynamo's platform and connected apps allow front-line teams to plan work, perform risk assessments, request work permits, record observations, get alerted on health and safety risks in real-time (e.g. fatigue or exclusion zones), and share lessons learnt. More ❯