Role overview

Qualifications

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field (or equivalent experience)
Hands-on experience with SQL, relational databases, and data modeling
Proficiency with modern ETL/ELT tools and data pipeline technologies (such as Python, Airflow, Spark, or similar)
Experience with cloud environments (AWS, Azure, or DoD specific cloud platforms)

Responsibilities

Design, develop, and maintain scalable data pipelines to ingest, transform, and curate logistics, maintenance, and supply chain data from multiple USMC systems
Implement robust ETL/ELT workflows to support analytics, reporting, and data warehousing initiatives
Collaborate with data analysts, software engineers, and system integrators to ensure high-quality data delivery for operational and strategic use cases
Ensure compliance with DoD cybersecurity, RMF, and data governance standards when designing and deploying solutions

Key facts

Remote from: West Virginia (USA), Anywhere
Full time
Data Engineer
English

Hard skills

Data Modeling SQL (Programming Language) Python (Programming Language) Apache Airflow Amazon Web Services Microsoft Azure Data Quality Data Governance Containerization CI/CD DevSecOps Root Cause Analysis

Other skills

Collaboration
Troubleshooting (Problem Solving)

About the company

Innovatus Technology Consulting

Company details

Company typeSME

Links

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Job description

Data Engineer to support the United States Marine Corps (USMC) Logistics Data Systems Program under NIWC Atlantic. The ideal candidate will play a critical role in designing, building, and maintaining data pipelines and analytics solutions that enhance logistics readiness, operational visibility, and datadriven decision-making across the Marine Corps enterprise.

***This is a remote role, with West Coast hours.***

Key Responsibilities

Design, develop, and maintain scalable data pipelines to ingest, transform, and curate logistics, maintenance, and supply chain data from multiple USMC systems.

Implement robust ETL/ELT workflows to support analytics, reporting, and data warehousing initiatives.

Collaborate with data analysts, software engineers, and system integrators to ensure high-quality data delivery for operational and strategic use cases.

Optimize database performance, manage data models, and ensure data accessibility and consistency across environments.

Work with NIWC Atlantic and USMC stakeholders to translate logistics data requirements into technical solutions.

Integrate data from authoritative USMC logistics systems such as GCSSMC, TLS, and other readiness and sustainment datasets.

Ensure compliance with DoD cybersecurity, RMF, and data governance standards when designing and deploying solutions.

Support the development of dashboards, APIs, and analytics tools that improve supply chain visibility and asset readiness.

Troubleshoot data issues, perform root cause analysis, and implement corrective actions to maintain data integrity.

Required Qualifications

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field (or equivalent experience).

Handson experience with SQL, relational databases, and data modeling.

Proficiency with modern ETL/ELT tools and data pipeline technologies (such as Python, Airflow, Spark, or similar).

Experience with cloud environments (AWS, Azure, or DoD specific cloud platforms).

Ability to work within Agile teams and collaborate with cross functional technical and government stakeholders.

Strong understanding of data quality, integrity, and governance principles.

Active DoD Secret Clearance (or ability to obtain and maintain one).

Preferred Qualifications

Experience supporting DoD, USMC, or logistics/sustainment data systems.

Familiarity with NIWC Atlantic processes, environments, and program expectations.

Exposure to GCSSMC, logistics readiness systems, or maintenance/supply chain data environments.

Experience with containerization, CI/CD pipelines, and DevSecOps concepts.

Knowledge of RMF, cybersecurity best practices, and secure data engineering approaches.

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