Director Data & Knowledge Management

Director Data & Knowledge Management

At North Wind, we are advancing hypersonic technologies through cutting-edge research, system development, and flight testing. Our team drives innovation in high-speed aerodynamics, propulsion systems, and mission-critical components, supporting every phase of hypersonic programs - from R&D to flight testing. If you are passionate about pushing technological boundaries in a dynamic environment, we invite you to join us.

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. The successful candidate will establish and lead a multidisciplinary team responsible for transforming complex, distributed, and heterogeneous engineering and mission data into governed, discoverable, reusable, and AI-ready information and knowledge products. This role encompasses the complete data lifecyclefrom acquisition, ingestion, transformation, curation, and storage through metadata, provenance, semantic modeling, knowledge representation, analytics, and intelligent retrieval.

The position requires a combination of technical depth, data science leadership, systems thinking, and organizational leadership. The Director will establish architectures and engineering practices that allow data and knowledge to be reliably used by engineers, analysts, data scientists, AI systems, and autonomous agents across on-premises, commercial cloud, government cloud, and regulated or classified environments.

Responsibilities

• The Director for Data & Knowledge Management will lead the development and operation of enterprise data and knowledge capabilities supporting advanced engineering, artificial intelligence, modeling and simulation, analytics, and mission applications. This role serves as the team leader for data engineering, data science, knowledge engineering, metadata and provenance management, data governance, and information retrieval capabilities.
• The Director will build and manage teams responsible for acquiring, processing, curating, governing, and exposing data from engineering systems, test environments, simulations, sensors, enterprise applications, and other authoritative sources. The organization will establish reusable data pipelines and data products while preserving the context, lineage, relationships, uncertainty, and provenance necessary to transform raw information into trusted engineering and mission knowledge.
• The successful candidate will provide technical leadership for modern data architectures including lakehouse and data fabric patterns, distributed data processing, semantic models, ontologies, knowledge graphs, vector and graph databases, and AI-enabled knowledge retrieval. Particular emphasis will be placed on making enterprise data accessible to AI/ML and agentic systems through governed APIs, retrieval-augmented generation (RAG), semantic search, knowledge graphs, and machine-readable context.
• The Director will also lead the application of data science and advanced analytics to extract information from complex datasets, identify relationships and patterns, develop predictive capabilities, and create reusable analytical methods and data products. The role requires the ability to bridge traditional data engineering with data science, knowledge representation, and emerging AI technologies.
• Working closely with infrastructure, cybersecurity, software, AI/ML, digital engineering, and mission teams, the Director will establish enterprise data standards, architecture patterns, governance mechanisms, and technical roadmaps while mentoring technical staff and developing a high-performing Data & Knowledge organization.

Key areas of responsibility include:

• 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

Education

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

Experience

• 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 and developing methods for transforming those datasets into actionable information.
• Experience supporting regulated, government, national security, or classified environments preferred.

Technical Skills

The ideal candidate possesses broad expertise spanning data science, data engineering, knowledge management, advanced analytics, artificial intelligence, and enterprise-scale information architectures. They should demonstrate the ability to lead teams that transform raw and heterogeneous information into trusted, contextualized, and reusable data and knowledge products. Success in this role requires more than traditional database or data warehouse expertise. The candidate should understand how information moves from physical systems, experiments, simulations, enterprise applications, and other authoritative sources through processing and analytical pipelines and ultimately becomes knowledge that can support human decision-making, machine learning, and autonomous AI systems. The candidate should have experience solving complex data problems requiring analytical reasoning, algorithm development, computational methods, and collaboration across multidisciplinary technical teams.

Key technical competencies include:

• 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