Director – Data & Knowledge Management

NORTH WIND SYSTEMS LLC•St. Paul, MN

2d•$135,000 - $250,000

About The Position

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 lifecycle—from 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.

Requirements

Bachelor's degree in Computer Science, Data Science, Physics, Mathematics, or other computational science, Data Engineering, Software Engineering, Information Systems, Engineering, or a related technical field.
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.
Broad expertise spanning data science, data engineering, knowledge management, advanced analytics, artificial intelligence, and enterprise-scale information architectures.
Ability to lead teams that transform raw and heterogeneous information into trusted, contextualized, and reusable data and knowledge products.
Understanding of 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.
Experience solving complex data problems requiring analytical reasoning, algorithm development, computational methods, and collaboration across multidisciplinary technical teams.
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
All candidates must be eligible to work in the United States.

Nice To Haves

Advanced technical degree preferred.
Experience supporting regulated, government, national security, or classified environments preferred.
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.
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.

Responsibilities

Lead the development and operation of enterprise data and knowledge capabilities supporting advanced engineering, artificial intelligence, modeling and simulation, analytics, and mission applications.
Serve as the team leader for data engineering, data science, knowledge engineering, metadata and provenance management, data governance, and information retrieval capabilities.
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.
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.
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.
Make enterprise data accessible to AI/ML and agentic systems through governed APIs, retrieval-augmented generation (RAG), semantic search, knowledge graphs, and machine-readable context.
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.
Bridge traditional data engineering with data science, knowledge representation, and emerging AI technologies.
Establish enterprise data standards, architecture patterns, governance mechanisms, and technical roadmaps.
Mentor technical staff and develop a high-performing Data & Knowledge organization.
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