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# Enterprise Data Systems Engineer

- **Company:** Arcfield, Inc.
- **Location:** Chantilly, VA, United States
- **Salary:** $94,911 - $165,031
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Data Analysis, Systems Engineering, Data Architecture, Data Governance, Data Integration, Data Structures, Data Visualization, Relational Databases, Decision Support Systems, Distributed Data Store, Graph Database, Systems Analysis, Interoperability, JSON, Python (Programming Language), Machine Learning, Metadata, Meta-Data Management, Metadata Repositories, Web Ontology Language, SPARQL, SQL Databases, Extensible Markup Language (XML), Enterprise Data Management, Cloud Platform System, Data Classification, Data Representation, Data Lakes, Data Lineage, Enterprise Integration, Data Management, Devsecops
- **Published:** November 11, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9305628/enterprise-data-systems-engineer

## About the Role

The ideal candidate has a strong technical foundation in data science and data architecture, with the ability to assess proposed solutions, identify integration challenges, and recommend practical improvements. Working alongside experienced systems engineers, architects, developers, and Government stakeholders, you will help ensure enterprise data capabilities are technically sound, scalable, and aligned with mission objectives ., * Clearance: Must possess and be able to maintain a TS/SCI with Polygraph.

 * Education & Experience: Bachelor's degree with 5-7 years of relevant experience; OR a Master's degree with 3-5 years of experience; OR PhD with 0-2 years of experience.

 * Data Architecture & Structures: Working knowledge of data models, schemas, relational databases, data structures, and integration principles.

 * Data Management: Understanding of metadata, data quality, data classification, and enterprise data organization concepts.

 * Technical Proficiency: Familiarity with SQL, Python, or comparable data analysis and processing technologies, sufficient to assess technical approaches and data workflows.

 * Systems Analysis: Ability to evaluate technical solutions, identify dependencies and risks, and contribute to engineering assessments and recommendations.

 * Communication & Collaboration: Strong analytical, documentation, and communication skills with the ability to work across multidisciplinary technical teams.

Desired Qualifications

 * Metadata & Semantic Technologies: Familiarity with data catalogs, ontologies, taxonomies, knowledge graphs, semantic models, or metadata management platforms.

 * Data Standards & Technologies: Understanding of RDF, OWL, SPARQL, JSON, XML, APIs, or related data representation and integration technologies.

 * Enterprise Data Platforms: Exposure to data lakes, warehouses, cloud-based data platforms, or distributed data architectures.

 * Data Governance: Familiarity with data lineage, provenance, master data management, data stewardship, or enterprise data standards.

 * Analytics & AI/ML: Understanding of statistical analysis, machine learning, AI-enabled applications, or data visualization techniques.

 * Mission Environment: Experience supporting Intelligence Community (IC) or DoD systems, enterprise integration, or Agile/ DevSecOps environments.

## Description

In this role, you will provide technical analysis and engineering advisory support to help the Government organize, integrate, and effectively leverage complex mission data across enterprise systems. You will evaluate data models, schemas, metadata, ontologies, and data catalogs to ensure technical approaches support interoperability, accessibility, consistency, and mission requirements., * Data Architecture & Modeling: Assess data models, schemas, structures, and relationships for consistency, interoperability, and alignment with enterprise requirements.

 * Metadata & Data Catalogs: Evaluate metadata management approaches, data catalogs, dictionaries, and common data definitions to improve enterprise data discovery and accessibility.

 * Ontologies & Semantic Data: Assess ontologies, taxonomies, semantic relationships, and data standards to support consistent interpretation and integration of mission data.

 * Data Integration & Interoperability: Analyze data flows, interfaces, mappings, and transformation approaches to identify dependencies, integration gaps, and technical risks.

 * Data Quality & Governance: Evaluate data quality, provenance, lineage, classification, and governance approaches to support reliable and traceable enterprise data.

 * Analytics & Emerging Capabilities: Assess data readiness and technical approaches supporting advanced analytics, automation, artificial intelligence, and machine learning.

 * Technical Requirements & Coordination: Collaborate with engineers, architects, developers, and mission stakeholders to define data requirements, evaluate proposed solutions, and resolve technical issues.

 * Engineering Assessment & Validation: Support technical reviews, testing assessments, and validation activities to evaluate data capabilities against requirements and mission objectives .

 * Government Decision Support: Develop technical assessments, documentation, recommendations, and briefings to inform Government decisions on enterprise data capabilities.