Full Stack Engineer

Kellton is seeking a Lead Cloud Full-Stack Engineer with strong cloud engineering experience. The role involves designing and operating cloud-native solutions, focusing on data ingestion, transformation, and creating intuitive dashboard experiences.

Responsibilities:

• Strong cloud full-stack engineering experience, with deep knowledge across backend services, data platforms, APIs, and front-end analytics applications
• You are comfortable designing and operating cloud-native solutions end to end—from data ingestion and transformation to secure service layers and intuitive dashboard experiences
• 12+ years of experience designing, building, and operating scalable, cloud-native applications, data pipelines, and analytics platforms in high-availability environments
• 3+ years of experience developing modern front-end applications using TypeScript and React, with a strong focus on analytics dashboards and data-driven interfaces
• Strong hands-on experience with backend technologies such as Node.js (preferably with TypeScript) and Python, building APIs, event-driven services, and data processing components that power real-time and near–real-time analytics
• Experience designing and maintaining reliable, scalable data ingestion, transformation, and orchestration pipelines to support operational and analytical workloads
• Expertise in developing responsive, secure, and high-performance user interfaces using TypeScript, JavaScript, HTML, and CSS
• Experience implementing role-based access control (RBAC) and secure access patterns to ensure proper data governance and protection of sensitive information
• Experience with asynchronous programming, event-driven architectures, and telemetry/event-streaming patterns
• Hands-on experience with real-time data monitoring and analytics platforms such as Grafana and InfluxDB
• Strong experience with cloud-based data stores and query engines such as Amazon Redshift, Athena, DynamoDB, and S3-based data lakes, including performance optimization and trend analysis
• Deep expertise in data modeling and transformation within AWS, leveraging services such as Glue, Redshift, Athena, EMR, Lambda, and S3 to build scalable, performant, and reliable analytical data foundations
• Experience implementing Machine Learning (ML) and Artificial Intelligence (AI) solutions within analytics platforms, including integrating predictive models, anomaly detection, trend analysis, or intelligent insights into production systems
• Familiarity with ML lifecycle practices, model deployment, monitoring, and operationalization using platforms such as SageMaker Studio, Amazon Quick Suite or similar environments
• Deep knowledge of AWS services including Lambda, SNS, SQS, S3, Step Functions, IAM, KMS, and CloudWatch
• Experience provisioning and managing cloud infrastructure using Infrastructure as Code tools such as AWS CDK, CloudFormation, Terraform, and AWS CLI

Requirements:

• 12+ years of experience designing, building, and operating scalable, cloud-native applications, data pipelines, and analytics platforms in high-availability environments
• 3+ years of experience developing modern front-end applications using TypeScript and React, with a strong focus on analytics dashboards and data-driven interfaces
• Strong hands-on experience with backend technologies such as Node.js (preferably with TypeScript) and Python, building APIs, event-driven services, and data processing components that power real-time and near–real-time analytics
• Experience designing and maintaining reliable, scalable data ingestion, transformation, and orchestration pipelines to support operational and analytical workloads
• Expertise in developing responsive, secure, and high-performance user interfaces using TypeScript, JavaScript, HTML, and CSS
• Experience implementing role-based access control (RBAC) and secure access patterns to ensure proper data governance and protection of sensitive information
• Experience with asynchronous programming, event-driven architectures, and telemetry/event-streaming patterns
• Hands-on experience with real-time data monitoring and analytics platforms such as Grafana and InfluxDB
• Strong experience with cloud-based data stores and query engines such as Amazon Redshift, Athena, DynamoDB, and S3-based data lakes, including performance optimization and trend analysis
• Deep expertise in data modeling and transformation within AWS, leveraging services such as Glue, Redshift, Athena, EMR, Lambda, and S3 to build scalable, performant, and reliable analytical data foundations
• Experience implementing Machine Learning (ML) and Artificial Intelligence (AI) solutions within analytics platforms, including integrating predictive models, anomaly detection, trend analysis, or intelligent insights into production systems
• Familiarity with ML lifecycle practices, model deployment, monitoring, and operationalization using platforms such as SageMaker Studio, Amazon Quick Suite or similar environments
• Deep knowledge of AWS services including Lambda, SNS, SQS, S3, Step Functions, IAM, KMS, and CloudWatch
• Experience provisioning and managing cloud infrastructure using Infrastructure as Code tools such as AWS CDK, CloudFormation, Terraform, and AWS CLI