Data Engineer; IMC

• 3+ years of experience in data engineering, data science, software development, or related discipline
• 1+ years of experience with Cloud-native data warehouse, or analytics platforms, and supporting data science applications and analytical tools
• Pursuant to a government contract, this specific position requires U.S. Citizenship
• Current DoD TS/SCI clearance eligibility day one and prior to entry on duty

The data engineer is responsible for designing, building, securing, and optimizing cloud-native data solutions in AWS that support data analytics, artificial intelligence, machine learning, and large language model (LLM) initiatives.

Note:

These are mandatory items that all candidates must have when submitting an application to IMC for this position. Please ensure that your submission addresses each of these requirement items. Candidates without these required elements will not be considered.

• Bachelor’s degree in computer science, data engineering, data science, computer engineering, or a related technical field.
• 3+ years of experience in data engineering, data science, software development, or related discipline, including hands-on software development lifecycle (SDLC) experience.
• 1+ years of experience with:
  • Cloud-native data warehouse, or analytics platforms, such as Amazon Redshift, Amazon Athena, AWS Glue Data Catalog, or AWS Lake Formation.
  • Supporting data science applications and analytical tools such as Python, Jupyter, Amazon Sage Maker Studio, AWS Glue Data Brew, or Amazon Quick Sight.
  • Implementing cloud-native data security controls, including IAM, encryption, and network segmentation.
• Experience preparing data for data scientists, including data preparation, feature engineering, and model-ready dataset construction.
• Ability to meet DoD 8140.03 requirements for the applicable work role, including Data Architect (Intermediate) or equivalent.
• Ability to obtain and maintain a TS/SCI with SAP eligibility.
• Pursuant to a government contract, this specific position requires U.S. Citizenship.
• All applicants must have current DoD TS/SCI clearance eligibility day one and prior to entry on duty.

• Experience with:
  • Amazon Bedrock, Knowledge Bases, RAG architectures, agentic services, or LLM prompt and token optimization.
  • AWS Security Hub, Amazon Guard Duty, AWS KMS, AWS IAM Identity Center, and AWS Certificate Manager.
  • Applying NIST SP 80-53 Rev. 5 and DoD Zero Trust Architecture principals.
  • Fin Ops dashboards and tools such as AWS Cost Explorer, AWS Budgets, and AWS Cost and Usage Reports
  • Data discovery, federated search, geospatial or non-geospatial search, and knowledge-management solutions.
  • Supporting classified or highly regulated DoD environments.
• AWS, data engineering, AI/ML, or DoD cybersecurity certifications preferred.

• Design, build, troubleshoot, and tune end-to-end cloud-native data engineering solutions within AWS.
• Develop and administer data ingestion, ETL, integrations, transformations, validations, publications, and data lifecycle 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, including Glue, Athena, Redshift, Kinesis, Lake Formation, Glue Data Catalog, RDS, Aurora, DynamoDB, and related technologies.
• Create REST APIs and web services to expose data to applications, analytics platforms, and downstream systems.
• Support AI/ML infrastructure, including model hosting, model-ready datasets, agentic workflow orchestration, RAG solutions, LLM integrations, and workflow orchestration.
• Develop automation, monitoring, alerting, and operational resilience using AWS-native tools such as Cloud Watch and Cloud Trail.
• Implement data discovery and search capabilities using services such as AWS Open Search, Glue Data Catalog, and Kendra.
• Apply Fin Ops best practices, including resource tagging, cost allocation, right-sizing, storage tiering, query optimization, budget monitoring, cost-per workload analysis, and AI/LLM cost tracking.
• Implement and maintain DoD Zero Trust and NIST 800-53 security controls across data engineering and AI environments.
• Configure identity and access management, least-privilege access, encryption at rest and in transit, network segmentation, certificate…