[Hiring] Director of Data & Knowledge Management @NORTH WIND SYSTEMS LLC

Role Description

We are seeking a highly skilled Director for Data & Knowledge Management to lead the architecture, development, integration, and operation of enterprise data and knowledge capabilities supporting Digital Engineering, AI/ML, Modeling & Simulation, advanced analytics, and mission-critical engineering applications. This role encompasses the complete data lifecycle—from acquisition, ingestion, transformation, curation, and storage through metadata, provenance, semantic modeling, knowledge representation, analytics, and intelligent retrieval.

Responsibilities

• Lead the development and operation of enterprise data and knowledge capabilities supporting advanced engineering, artificial intelligence, modeling and simulation, analytics, and mission applications.
• Build and manage teams responsible for acquiring, processing, curating, governing, and exposing data from various sources.
• Establish reusable data pipelines and data products while preserving context, lineage, relationships, uncertainty, and provenance.
• Provide technical leadership for modern data architectures including lakehouse and data fabric patterns.
• Lead the application of data science and advanced analytics to extract information from complex datasets.
• Establish enterprise data standards, architecture patterns, governance mechanisms, and technical roadmaps.

Key Areas of Responsibility

• Enterprise data and knowledge architecture
• Data engineering, ingestion, transformation, and integration
• Data lakehouse, data fabric, and distributed data architectures
• Data science, advanced analytics, and algorithm development
• Metadata, provenance, lineage, and data lifecycle management
• Data quality, validation, curation, and authoritative-source management
• Ontology, taxonomy, and semantic model development
• Knowledge graphs and graph-based data architectures
• Vector databases, embeddings, and semantic retrieval
• Retrieval-Augmented Generation (RAG) and AI knowledge services
• Data preparation and knowledge grounding for AI/ML and agentic systems
• Engineering and mission data product development
• Data governance, access policy, and stewardship
• Scientific, engineering, test, and simulation data management
• Data compression, optimization, and efficient information representation
• Cross-domain data movement and distributed data management
• Technical leadership, team development, mentoring, and architecture governance

Qualifications

• Bachelor's degree in Computer Science, Data Science, Physics, Mathematics, Data Engineering, Software Engineering, Information Systems, Engineering, or a related technical field.
• Advanced technical degree preferred.

Requirements

• 10+ years of experience in one or more of: Data Science, Data Engineering, Knowledge Engineering, Advanced Analytics, Scientific Computing, Enterprise Data Architecture, AI/ML Engineering, Technical or Research & Development Leadership.
• 5+ years of experience leading technical teams, research teams, or multidisciplinary engineering organizations.
• Demonstrated experience developing data-driven solutions for complex scientific, engineering, national security, or mission applications.
• Experience working with large, complex, heterogeneous, or high-value datasets.
• Experience supporting regulated, government, national security, or classified environments preferred.

Technical Skills

• Data science, statistical analysis, and advanced analytics
• Data engineering and scalable data processing
• Data pipeline and workflow development
• Data lakehouse, data fabric, and modern enterprise data architectures
• Structured, semi-structured, unstructured, time-series, and scientific data
• Relational, document, graph, vector, and distributed database technologies
• Metadata management, provenance, lineage, and data catalogs
• Data governance, stewardship, quality, and lifecycle management
• Ontologies, taxonomies, semantic models, and knowledge representation
• Knowledge graph architecture and graph analytics
• Machine learning and AI-ready data engineering
• Embeddings, vector search, semantic search, and information retrieval
• Retrieval-Augmented Generation (RAG) architectures
• Knowledge grounding and context engineering for AI and agentic systems
• Agentic data discovery, reasoning, and knowledge workflows
• Python and modern data science/software development environments
• APIs and programmatic data access
• Distributed computing and high-performance data processing
• Scientific and engineering data processing
• Data compression, representation, and storage optimization
• Data visualization and communication of complex analytical results
• Technical architecture, systems integration, and enterprise-scale solution design

Preferred Qualifications

• Master's degree or Ph.D. in a relevant scientific, engineering, computational, or data discipline
• Active Top Secret Clearance
• Experience leading data science or advanced analytics teams
• Experience supporting Department of Defense, Department of Energy, Intelligence Community, or other national security missions
• Experience with scientific, experimental, sensor, or engineering datasets
• Experience developing novel algorithms or intellectual property
• Experience with knowledge graphs, ontologies, semantic technologies, or graph analytics
• Experience with modern AI/ML architectures and generative AI
• Experience implementing RAG, semantic retrieval, or AI knowledge systems
• Experience with data lakehouse and distributed data processing technologies
• Experience integrating data across cloud, on-premises, and restricted environments
• Experience transitioning research concepts into operational capabilities

Desired Characteristics

• Strong scientific and analytical problem-solving mindset
• Able to bridge data science, data engineering, knowledge engineering, and AI
• Comfortable moving from fundamental technical problems through operational implementation
• Capable of building and leading multidisciplinary technical teams
• Understands that data must retain context, provenance, relationships, and meaning to become reusable knowledge
• Able to translate complex mission and engineering problems into scalable data and knowledge architectures
• Comfortable working with researchers, engineers, data scientists, software developers, program managers, customers, and executive leadership
• Strong technical judgment with the ability to balance research innovation and production reliability
• Passion for developing reusable capabilities rather than isolated point solutions
• Ability to mentor technical staff and establish engineering standards across multiple teams
• Comfortable operating in rapidly evolving technical environments where AI, data science, and knowledge technologies increasingly converge