Systems Data Engineering and Integrator

CACI•Suitland, MD

1d•Onsite

About The Position

CACI is seeking a highly motivated Systems Integration & Data Pipeline Engineer with an active TS/SCI clearance to support a flagship data-modernization and AI/ML-enablement effort within the Department of Defense (DoD) and Intelligence Community (IC). In this hybrid engineering and analytical role, you will design and deploy modern data pipelines, transform static datasets into AI-ready assets, and build automated quality-readiness scorecards that drive mission-critical decisions. You will assess full data lifecycles, identify performance and lineage gaps, and collaborate directly with Data Scientists to ensure data is optimized for high-value AI/ML workloads. This position requires 100% onsite work in a secure facility, with approximately 10% local travel to customer locations.

Requirements

Active TS/SCI Clearance: A current, verifiable DoD TS/SCI clearance is required prior to onboarding.
Bachelor’s degree in an engineering or technical discipline such as Computer Science, Systems Engineering, Data Engineering, Software Engineering, or Information Systems.
At least 5 years of hands-on experience in data engineering, backend development, or systems integration—preferably within DoD or Intelligence Community environments.
Proven ability to analyze complex data ecosystems, assess infrastructure dependencies, and provide structured technical insights to cross-functional teams.
Demonstrated success partnering with Data Scientists, analysts, or AI/ML practitioners to deliver production-ready, high-quality data structures.
Current CompTIA Security+ CE or higher DoD 8570 IAT Level II certification.

Responsibilities

Build and Optimize Modern Data Pipelines: Design, engineer, test, and maintain automated ETL/ELT pipelines that transition data from siloed legacy systems into secure, scalable, high-performance environments.
Conduct End-to-End Systems Analysis: Evaluate the full data journey—from ingestion to consumption—identifying performance bottlenecks, lineage gaps, and infrastructure constraints. Deliver clear engineering recommendations that strengthen reliability and throughput.
Collaborate Directly With Data Scientists: Translate analytical and machine learning requirements into pipeline and system enhancements. Shape the data environments that fuel next-generation AI/ML models.
Implement Automated Data Quality Gateways: Develop code-driven readiness gates that enforce strict data quality standards, including Accuracy, Completeness, Consistency, Timeliness, and Validity.
Support AI/ML Tooling and Infrastructure: Build data storage, orchestration, and feature-generation pipelines that prepare datasets for training, testing, and deploying advanced ML models.
Integrate Systems and Modernize Platforms: Develop APIs, connectors, orchestration processes, and system-to-system integrations that unify previously disconnected platforms into a cohesive, self-service data ecosystem.
Enforce IC and DoW Data Standards: Implement schemas, metadata tagging strategies, and automated formatting rules to ensure all ingested data complies with Intelligence Community and Department of War specifications.
Document Technical Architecture and Flows: Create clean, version-controlled documentation covering pipeline architecture, system configurations, API mappings, and data lineage diagrams.