76 to 100 of 366 Remote/Hybrid DynamoDB Jobs

adoption, and performance metrics.QUALIFICATIONSSkills and AbilitiesStrong experience with Python for backend development Proven expertise in AWS Serverless technologies: Lambda, API Gateway, AppSync, Step Functions, DynamoDB, etc. Strong experience with microservices architecture and event-driven systems Hands-on experience with Infrastructure as Code (IaC) tools such ...

adoption, and performance metrics.QUALIFICATIONSSkills and AbilitiesStrong experience with Python for backend development Proven expertise in AWS Serverless technologies: Lambda, API Gateway, AppSync, Step Functions, DynamoDB, etc. Strong experience with microservices architecture and event-driven systems Hands-on experience with Infrastructure as Code (IaC) tools such ...

challenge the status quo. Experience developing and deploying cloud native software on the AWS cloud. Experience using serverless technologies like Lambda, API Gateway, and DynamoDB is preferred. Experience with UI development using ReactJS and/or React Native. Gets excited by the challenge of complex technical problems. Competent with ...

cloud and contact centre expertise. Skills Knowledge and Expertise Extensive experience with Amazon Connect. Extensive experience with AWS services i.e. Lambda, CloudWatch , API Gateway, DynamoDB, IAM and S3. Extensive experience with contact center standards and systems. Strong understanding of CCaaS platforms and customer experience solutions. Extensive experience in consultative ...

system design and distributed systems knowledge You have experience with SQL and relational databases You have experience with production AWS - CI/CD, SQS, DynamoDB, S3, ECS or Lambda advantageous You have a good understanding of microservices and event driven architectures You're comfortable taking technical ownership and mentoring ...

Vue.js, React or modern Angular You have a good knowledge of SQL and relational databases You have production experience with AWS, ideally including SQS, DynamoDB, S3, ECS and Lambda You have experience owning CI/CD and release processes, such as GitHub Actions You have hands-on experience with ...

like - Hive/Hadoop, Apache SparkExperience in developing Finance or HR related applicationsExperience with following cloud services: AWS Elastic Beanstalk, EC2, S3, CloudFront, RDS, DynamoDB, VPC, Elastic Cache, LambdaWorking experience with TerraformExperience in creating workflows for Apache Airflow#LI-GL1What's Roku's approach to hybrid working? Roku fosters ...

like - Hive/Hadoop, Apache SparkExperience in developing Finance or HR related applicationsExperience with following cloud services: AWS Elastic Beanstalk, EC2, S3, CloudFront, RDS, DynamoDB, VPC, Elastic Cache, LambdaWorking experience with TerraformExperience in creating workflows for Apache Airflow#LI-GL1What's Roku's approach to hybrid working? Roku fosters ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...

Rails codebase, even if it's not where you've spent the most time. Cloud Native: Deep AWS across Lambda, S3, SQS, Kinesis, EventBridge, DynamoDB, Aurora/RDS, Redshift, and IAM. ML in Production: You've kept models serving correctly over time - versioning, dependency drift, feature plumbing, explainability. ...