AWS Cloud Data Engineer

About the role

DNI is seeking a highly qualified Data Engineer to support the Joint Warfare Analysis Center (JWAC) under Air Force JWAC IT Services (JITS) Task Order 2. The Data Engineer will design, implement, secure, monitor, and optimize cloud-native data engineering solutions that support data science, artificial intelligence/machine learning (AI/ML), agentic services, hosted models, and large language model (LLM) optimization. The role integrates data engineering, FinOps, Zero Trust security, and AI/ML infrastructure responsibilities within a single position. This position is expected to work onsite at JWAC in Dahlgren, Virginia, supporting a TS/SCI/SAP environment.

Responsibilities

• Design, build, troubleshoot, and tune end-to-end cloud-native data engineering solutions using AWS platform and software services
• Develop and administer data ingestion, ETL, integration, transformation, validation, publication, and lifecycle-management workflows
• Integrate structured and unstructured data from databases, web services, message traffic, data dumps, documents, and other sources
• Design data architectures and managed data stores that support analytics, feature engineering, model training, and real-time inference
• Develop solutions using AWS services such as AWS Glue, Amazon Athena, Amazon Redshift, Amazon Kinesis, AWS Lake Formation, AWS Glue Data Catalog, Amazon RDS, Amazon Aurora, and Amazon DynamoDB
• Create REST APIs and web services to expose data to JWAC-developed applications and analytical systems
• Support AI/ML infrastructure, including model hosting, model-ready datasets, agentic workflow orchestration, retrieval-augmented generation (RAG), and LLM integration and optimization
• Develop automation, monitoring, alerting, and self-healing capabilities using AWS-native tools such as Amazon CloudWatch and AWS CloudTrail
• Implement data discovery and search capabilities using services such as Amazon OpenSearch Service, AWS Glue Data Catalog, and Amazon Kendra
• Apply FinOps practices, including resource tagging, cost allocation, right-sizing, storage tiering, query optimization, budget monitoring, cost-per-workload analysis, and AI/LLM cost tracking
• Implement DoD Zero Trust and NIST SP 800-53 Rev. 5 security controls across data engineering and AI service environments
• Configure identity and access management, least-privilege access, encryption at rest and in transit, network segmentation, certificate management, and security monitoring