The Data Architect must have 8 years of demonstrated experience in data architecture, data engineering, or enterprise systems design. Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field.Experience working in defense, intelligence, or other high-security environments. Strong knowledge of database systems (SQL/NoSQL), data warehousing, and distributed data platforms. Familiarity with cloud platforms (AWS GovCloud, Azure Government, etc.). Knowledge of data governance, data modeling, and architecture frameworks (e.g., TOGAF, DoDAF). Ability to build and maintain DevSecOps CI/CD build pipelines. Active Top Secret clearance is required.
Responsibilities will include, but are not limited to the following:
- Design and implement enterprise-level data architecture frameworks across defense systems and platforms
- Develop data models, standards, and governance policies to ensure consistency, quality, and integrity
- Integrate data from multiple classified and unclassified sources to support operational readiness and analysis
- Collaborate with cybersecurity teams to ensure compliance with security protocols and data protection regulations
- Support advanced analytics, AI/ML initiatives, and real-time data processing for research and strategic applications
- Establish metadata management, data lineage, and master data management practices
- Optimize data storage, retrieval, and processing for high-performance environments
- Lead data architecture reviews and provide technical guidance to engineering and analytics teams
- Ensure compliance with federal standards (e.g., DoD data strategy, security frameworks, interoperability requirements)
- Experience with big data technologies (Hadoop, Spark, Karka)
Preferred Qualifications- Background in AI/ML data pipelines and analytics infrastructure
- Familiarity with geospatial, signals, or intelligence data systems
- Certification in cloud architecture or data management (e.g., AWS Certified Solutions Architect, DAMA-CDMP)
- Experience with high-performance computing (HPC)
- Experience with Infrastructure as-Code practices
- Proficiency in SPARQL query language
- Strong familiarity with implementation techniques used in broad array of package managers (npm, pip, etc.)
- Familiarity with applying library science principles such as data catalog curation, reference interview, and Functional Requirements for Biblographic Records (FRBR)
- Strong operational knowledge of the architecture (not practice) of the World Wide Web and its semiological distinction between resources and their representations.
- Experience in applying Basic Formal Ontology (BFO) concepts within and among existing semi-mutable infrastructure to achieve quantified value add
- Active TS/SCI clearance.
