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Software Engineer with Security Clearance

ALKUSuitland, MD🇺🇸United StatesPosted 19 Aug 2026

Why This Role Stands Out

This hybrid role offers a fantastic opportunity to leverage your engineering and analytical skills in designing and deploying cutting-edge data pipelines that power AI/ML initiatives, with direct impact on mission-critical decisions. If you thrive in collaborative environments, enjoy solving complex data challenges, and possess a security clearance, this position is perfect for you to grow your expertise and contribute to impactful work. Apply now to join a forward-thinking team and shape the future of data-driven innovation!

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Suitland, MD, United States
Posted
1 week ago
ETLMachine Learning

Job Description

Software Developer Program Description: 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.
Day to Day 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.

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