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# Cloud Engineer - AWS Data Platforms

- **Company:** DNI Delaware Nation Industries
- **Location:** United States
- **Experience:** Experienced
- **Salary:** $150,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Automation of Tests, Cloud Computing Security, Configuration Management, Computer Engineering, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Digital Architecture, Identity and Access Management, Machine Learning, Azure Machine Learning, SAP (Applications), Systems Integration, Data Logging, Scripting, Cloud Platform System, Data Ingestion, Cloud Monitoring, Amazon Relational Database Service, Kubernetes, Infrastructure Automation Frameworks, Information Technology, AWS Lake Formation, AWS Glue, Machine Learning Operations, Data Pipelines, Devsecops, Serverless Computing, Amazon Redshift
- **Published:** October 8, 2026
- **Apply:** https://www.dice.com/job-detail/1ec33963-775d-4439-bb95-c87b7331f4f6

## About the Role

* Bachelor's degree in computer science, computer engineering, data science, or a related technical discipline.
 * Three or more years of relevant cloud, platform, infrastructure, or software-delivery experience.
 * Hands-on experience designing, deploying, or operating AWS cloud services; experience must include building or automating delivery, not only routine administration.
 * Demonstrated coding or scripting experience used for infrastructure automation, integrations, deployment, monitoring, or operational workflows.
 * Experience with cloud security fundamentals, such as identity and access management, encryption, network controls, logging, or continuous monitoring.
 * Interest in supporting data pipelines, ETL, analytics, or AI/ML services in cloud environments.
 * Ability to work onsite at JWAC in Dahlgren, Virginia, and to obtain and maintain the required TS/SCI and SAP access, as applicable to the assignment.
 * Ability to meet the DoD workforce qualification requirements applicable to the assigned duties; final work-role details must be confirmed before posting., * AWS experience with data, serverless, analytics, or integration services such as AWS Glue, Amazon Athena, Amazon Redshift, Amazon Kinesis, AWS Lake Formation, or Amazon RDS.
 * Kubernetes or container-platform delivery experience, especially deploying and operating workloads in AWS.
 * Experience with infrastructure-as-code, CI/CD, configuration management, automated testing, or DevSecOps.
 * Experience with cloud monitoring, alerting, backup and recovery, cost tracking, or FinOps practices.
 * Experience enabling data engineering, data science, search, or AI/ML workloads-including model hosting or retrieval/search integrations.
 * Experience in classified, regulated, or Department of Defense environments.
 * Relevant AWS, Kubernetes, cloud-security, or infrastructure certifications.

## Description

* Design, deploy, and maintain AWS cloud-native environments for data ingestion, processing, storage, search, analytics, and AI/ML workloads.
 * Use AWS managed, serverless, and platform services to build cost-aware, scalable solutions; contribute to services such as AWS Glue, Amazon Athena, Amazon Redshift, Amazon Kinesis, AWS Lake Formation, and Amazon RDS as appropriate.
 * Automate infrastructure provisioning, configuration, deployment, monitoring, and recovery; use infrastructure-as-code and CI/CD practices where applicable.
 * Support containerized or Kubernetes-based workloads alongside AWS-native services, selecting practical deployment patterns for each use case.
 * Partner with developers to deploy and operate data pipelines, APIs, integrations, and workflow automation; troubleshoot the cloud platform and application boundary.
 * Apply identity and access controls, encryption, network protections, logging, monitoring, and security checks in cloud delivery and operations.
 * Monitor availability, performance, utilization, and cost; recommend right-sizing and managed or serverless alternatives where appropriate.
 * Support cloud-hosted analytics and AI/ML capabilities, including data-preparation workflows, search, and model-serving integrations.
 * Document architecture, deployment decisions, operating procedures, risks, and cost considerations; communicate clearly with technical and Government stakeholders.