Principal Multi-Cloud Architect

CyberData Technologies is seeking a Principal Multi-Cloud Architect to serve as the cloud architecture authority for a federal research organization operating across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The architect will advise Government leadership, security personnel, researchers, and system owners on cloud architecture, modernization, security, and technical risk. This is a hands-on architecture role—not a purely advisory position. The architect will lead secure cloud design, Infrastructure as Code (IaC), CI/CD, cloud security integration, observability, secure AI architecture, AI governance, incident response, and technical modernization. The successful candidate must be able to translate federal and research requirements into practical, secure, and supportable cloud solutions. Primary Responsibilities Lead architecture and engineering across AWS, Azure, and GCP. Design secure, scalable, resilient, and cost-conscious cloud solutions. Lead complex troubleshooting, root-cause analysis, performance remediation, and technical recovery across multi-cloud environments. Develop and govern reusable Terraform modules and Infrastructure-as-Code standards. Establish and improve CI/CD pipelines using GitHub Enterprise Cloud or comparable platforms. Deploy and manage containerized workloads using Amazon EKS and related container orchestration services. Design cloud identity, networking, private connectivity, encryption, logging, and access control patterns. Apply FISMA, NIST, FedRAMP, Zero Trust, and TIC 3.0 requirements to cloud implementations. Develop and document Policy Enforcement Points consistent with TIC 3.0 reference architectures. Support the development and maintenance of security assessment and authorization artifacts, including control descriptions, configuration baselines, POA&M documentation, compensating controls, and cloud authorization documentation. Support AI governance documentation, risk assessments, and authorization artifacts for AI/ML workloads consistent with the NIST AI Risk Management Framework and applicable federal guidance. Architect secure cloud AI deployments, including private connectivity, identity-based access controls, data residency controls, logging, and telemetry integration. Integrate cloud telemetry and security events with Splunk, OpenSearch, and cloud-native monitoring services. Develop dashboards, operational runbooks, and automated response workflows supporting federal logging and monitoring requirements. Support vulnerability remediation, continuous monitoring, incident investigation, and corrective actions. Establish and lead a Cloud Computing Working Group to promote collaboration, information sharing, and lessons learned. Represent the program in enterprise initiatives, technical working groups, and communities of practice. Advise customer leadership, security personnel, researchers, and system owners on cloud architecture, modernization, and technical risk. Support scientific and research workloads, including large datasets and specialized research cloud platforms. Contribute to semiannual cloud optimization assessments evaluating process efficiency, resource utilization, security, performance, and cost. Develop architecture diagrams, implementation plans, security documentation, SOPs, and technical briefings. Provide limited after-hours support for significant incidents, maintenance, or operational emergencies. Required Qualifications Bachelor's degree in computer science, engineering, cybersecurity, information technology, or a related discipline, or equivalent relevant experience. Ten or more years of relevant cloud, infrastructure, systems engineering, or cybersecurity experience. Five or more years designing or operating production cloud environments. Deep architecture or engineering experience with at least two major cloud providers—AWS, Azure, or GCP—with sufficient knowledge of the third to lead an integrated multi-cloud architecture and operating model. Hands-on experience with Terraform and Infrastructure as Code. Experience building or governing CI/CD pipelines using GitHub, GitLab, Azure DevOps, or a comparable platform. Strong knowledge of cloud networking, identity and access management, encryption, logging, monitoring, and configuration security. Working knowledge of FISMA, NIST security controls, federal assessment and authorization, and continuous monitoring. Experience developing control descriptions or comparable cloud authorization documentation. Experience integrating cloud logs and security telemetry with Splunk, OpenSearch, or another enterprise monitoring platform. Familiarity with federal AI governance frameworks, including the NIST AI Risk Management Framework, and the ability to apply them to AI/ML risk and authorization documentation. Ability to troubleshoot complex cloud, security, networking, performance, and integration issues. Strong technical writing and stakeholder communication skills. Ability to lead technical working groups and coordinate across organizational boundaries without direct line authority. Ability to obtain and maintain required federal suitability determination and system access. Preferred Qualifications Prior federal health, research, or other regulated federal cloud experience. Experience supporting biomedical, scientific, research, or research computing environments. Experience with enterprise cloud research platforms, large-scale research data environments, or cloud-based scientific computing platforms. Experience with AWS GuardDuty, Detective, Security Hub, Inspector, CloudTrail, Config, ECS/EKS, Lambda, S3, RDS, or Bedrock. Experience with Azure Entra ID, Privileged Identity Management, ExpressRoute, Private Link, Azure OpenAI, or Azure Functions. Experience with GCP IAM, BigQuery, Vertex AI, or research data platforms. Experience implementing Zero Trust or TIC 3.0 architectures. Experience with Splunk SOAR or comparable security orchestration and automated response tooling. Familiarity with Kion, FinOps practices, cloud cost governance, budget enforcement, and cloud optimization methods. Familiarity with secure AI platform architectures using AWS Bedrock, Azure OpenAI, GCP Vertex AI, LiteLLM, or LibreChat. Preferred Certifications An architect-level certification from at least one major cloud provider is strongly preferred. Other relevant certifications include: AWS Certified Solutions Architect - Professional Microsoft Certified: Azure Solutions Architect Expert Google Professional Cloud Architect Certified Cloud Security Professional (CCSP) HashiCorp Certified: Terraform Associate Splunk Core Certified Power User or Splunk SOAR Certified Automation Developer CISSP, CISM, or comparable security certification #J-18808-Ljbffr Cyberdata Technologies, Inc.