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Contract Knowledge Graph Jobs in Virginia (NOW HIRING)

Knowledge of graph data science (GDS) and knowledge graph applications supporting AI/ML or MBSE ... and contract considerations. Depending on the position, employees may be eligible for overtime ...

Knowledge of graph data science (GDS) and knowledge graph applications supporting AI/ML or MBSE ... and contract considerations. Depending on the position, employees may be eligible for overtime ...

Knowledge of graph data science (GDS) and knowledge graph applications supporting AI/ML or MBSE ... and contract considerations. Depending on the position, employees may be eligible for overtime ...

Knowledge of graph data science (GDS) and knowledge graph applications supporting AI/ML or MBSE ... and contract considerations. Depending on the position, employees may be eligible for overtime ...

Knowledge of graph data science (GDS) and knowledge graph applications supporting AI/ML or MBSE ... and contract considerations. Depending on the position, employees may be eligible for overtime ...

... knowledge graph technologies. * Manage cross-functional agile delivery teams specializing in ... as contract provisions regarding labor categories that are specific to the position. The pay range ...

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Contract Knowledge Graph information

What are the key skills and qualifications needed to thrive as a Contract Knowledge Graph Specialist, and why are they important?

To excel as a Contract Knowledge Graph Specialist, you need expertise in semantic data modeling, contract analysis, and a background in computer science or information management. Familiarity with tools like Neo4j, RDF, SPARQL, and experience with natural language processing (NLP) solutions are typically required. Strong analytical thinking, attention to detail, and effective communication skills help translate complex contract terms into structured, actionable data. These competencies ensure accurate, scalable contract data representation, enabling better compliance, searchability, and automation for organizations.

How does a Contract Knowledge Graph specialist typically collaborate with legal and IT teams during implementation projects?

A Contract Knowledge Graph specialist often works closely with legal teams to understand contract structures, key clauses, and compliance requirements, ensuring that the graph accurately represents legal relationships and obligations. Simultaneously, they partner with IT and data teams to integrate data sources, design the graph schema, and implement technical solutions. Effective collaboration requires clear communication, regular meetings, and the ability to translate legal concepts into technical requirements, which helps ensure the knowledge graph delivers actionable insights for both legal and business stakeholders.

What is a Contract Knowledge Graph?

A Contract Knowledge Graph is a structured representation of the information and relationships found within contracts. It uses graph technology to map entities such as parties, clauses, obligations, and deadlines, and the connections between them. This makes it easier to search, analyze, and visualize contractual data, enabling more efficient compliance checks, risk assessments, and contract lifecycle management. Organizations use contract knowledge graphs to gain better insights, automate contract analysis, and improve decision-making processes.

What is the difference between Contract Knowledge Graph vs Contract Analyst?

AspectContract Knowledge GraphContract Analyst
Required CredentialsTypically a background in data science, knowledge graphs, or related fieldsUsually a degree in law, business, or finance
Work EnvironmentData-driven, often in tech or AI-focused teamsLegal, finance, or corporate departments
Employer & Industry UsageTech companies, AI firms, legal techCorporations, law firms, government agencies
Common Search & Comparison IntentUnderstanding data modeling and AI applications in contractsAnalyzing contract terms and compliance

The Contract Knowledge Graph focuses on creating structured, interconnected data models for contracts using AI and data science skills. In contrast, a Contract Analyst primarily reviews, interprets, and manages contract data within legal or business contexts. While both roles deal with contracts, the Knowledge Graph role emphasizes data structuring and AI, whereas the Analyst role centers on contract review and analysis.

What are the most commonly searched types of Knowledge Graph jobs in Virginia? The most popular types of Knowledge Graph jobs in Virginia are:
What job categories do people searching Contract Knowledge Graph jobs in Virginia look for? The top searched job categories for Contract Knowledge Graph jobs in Virginia are:
What cities in Virginia are hiring for Contract Knowledge Graph jobs? Cities in Virginia with the most Contract Knowledge Graph job openings:
Infographic showing various Contract Knowledge Graph job openings in Virginia as of May 2026, with employment types broken down into 62% Full Time, 15% Part Time, and 23% Contract. Highlights an 70% Physical, 26% Hybrid, and 4% Remote job distribution.
Head of Data, Platform and Intelligence

Head of Data, Platform and Intelligence

Babel Street

Reston, VA • Hybrid

Other

Medical, Dental, Vision, Life, Retirement

Posted 13 days ago


Job description

ROLE SUMMARY 

As Head of Data you will define and lead Babel Street's North Star Data Strategy and Architecture, building a cohesive, scalable, and AI-native data platform that transforms fragmented systems into a unified foundation for intelligence, analytics, and product innovation. 

You will own the full lifecycle of data across the organization, from ingestion and storage to semantics, retrieval, and productization. Your mandate is to unify fragmented systems into a cohesive, scalable, and AI-ready data platform that directly enables investigative, analytical, and operational workflows across Babel Street's product suite. 

You will work closely with Product and Engineering leadership, to ensure that data is not just infrastructure, but a core competitive advantage. This includes powering Babel Street's Knowledge Graph, enabling agentic and generative AI systems, and delivering data capabilities that are reliable, performant, and economically efficient at scale. 

This role requires deep technical expertise, strong architectural judgment, and the ability to translate complex data challenges into customer-impacting intelligence capabilities. 

This hybrid role will be based in our Reston, VA or Somerville, MA office. 

ROLE SPAN 

This role spans four integrated domains: 

1. Data Platform & Storage Architecture 

You will help define and evolve Babel Street's unified data platform, consolidating warehouse, search and object storage systems into a cohesive scalable foundation. This includes establishing clear patterns for when and how to use analytical warehouses, search/index systems, and object storage to support diverse workloads across the business. 

You will architect systems that operate at petabyte scale, ensuring high performance, reliability, and flexibility across batch and real-time data. A key focus will be driving platform rationalization while maintaining continuity of operations and minimizing risk. 

You will also establish standards for data ingestion, transformation, and lifecycle management, ensuring consistency and efficiency across the platform.  

2. Data Semantics, Knowledge Graph & Identity 

You will own the semantic foundation of Babel Street's data ecosystem, defining how data is modeled, connected, and understood across products and systems.  

This includes building and evolving the company's knowledge graph, including entity resolution, identity modeling, and relationship mapping across disparate data sources. You will establish ontology and schema strategies that ensure consistent interpretation of data across teams, products, and AI systems. 

Your work will enable graph-integrated reasoning and provide the structures context required for intelligence workflows and AI-driven applications. 

3. Data Access, Retrieval & AI-Enablement 

You will design and operate data access patterns that power both human and machine consumption of data, including API's, query layers and retrieval systems.  

This includes enabling hybrid retrieval approaches across structured, unstructured and vector-based data to support LLMs, RAG pipelines, and agentic systems. You will ensure that data is accessible in a way that is performant, scalable, and optimized for AI workloads. 

You will partner closely with AI and Applied ML teams to ensure seamless integration between data systems and model-driven capabilities, enabling reliable, explainable, and efficient intelligence generation. 

4. Data Productization, Governance & Economics 

You will establish a data-as-a-product operating model, ensuring that data assets are discoverable, reusable, and governed with clear ownership and accountability.  

This includes defining contracts, enforcing quality standards, and implementing metadata and governance frameworks that scale across the organization. 

You will also own the economics of the data platform, ensure efficient use of storage and compute, and optimize cost per query, cost per workload, and overall system efficiency. A key focus will be enabling scalable AI usage through efficient data retrieval and storage strategies. 

KEY RESPONSIBILITIES 

Define the North Star Data Architecture 

  • Establish and evolve the target-state data architecture, aligning storage, compute, search, and access patterns into a unified platform
  • Drive architectural clarity across warehouse, search, object storage, and real-time systems
  • Ensure consistency in schemas, metadata, and governance frameworks across all data domains 

Build an AI-Native Data Foundation 

  • Design a data platform optimized for AI and agentic workloads, including: 
    API-first, agent-callable data services
  • Hybrid retrieval patterns (search + analytical + vector)
  • Real-time and batch data unification
  • Enable scalable support for LLMs, RAG pipelines, and intelligence workflows 

Own Data as a Product 

  • Establish a data-as-a-product operating model, enabling discoverable, reusable, and well-governed data assets
  • Define and standardize data contracts, ownership models, and domain boundaries
  • Translate platform capabilities into customer-facing data products and differentiators 

Lead Platform Rationalization and Evolution 

  • Rationalize and evolve the current ecosystem (e.g., BigQuery, Elasticsearch/OpenSearch, S3) into a cohesive and cost-efficient architecture
  • Lead phased, low-risk migrations and consolidations aligned to business priorities
  • Balance short-term pragmatism with long-term architectural integrity 

Own Performance, Reliability, and Cost Economics 

  • Accountable for performance, scalability, and reliability of all data systems
  • Establish clear unit economics for data (e.g., cost per query, cost per workload, storage efficiency)
  • Implement strong observability, SLOs, and incident management practices 

Required Qualifications: 

  • 10+ years of experience in data platforms, data engineering, or distributed systems
  • Strong background working within multi-cloud or hybrid environments, including hands-on experience with Google Cloud Platform (GCP)
  • Proven experience designing and evolving large-scale data platforms through major architectural transitions (e.g., warehouse, search, Lakehouse, or multi-cloud transformations)
  • Deep expertise across multiple data paradigms, including:
  • Analytical warehouses
  • Search/index systems
  • Object storage and distributed data systems
  • Experience building platforms that support AI, ML, or agent-driven systems
  • Familiarity with vector search, retrieval architectures, and modern AI data patterns
  • Experience with graph-based data models, entity resolution, or knowledge graphs
  • Strong communication skills and the ability to collaborate effectively across technical and non-technical teams.
  • Experience operating in regulated, high-stakes, or mission-critical environments is strongly preferred. 

EDUCATION 

Bachelor's degree in Computer Science, Engineering, or a related technical field required. 
Master's degree or PhD preferred. 

Benefits at Babel Street (just to name a few...)

  • Health Benefits: Babel Street covers 85-100% monthly premium costs for Medical, Dental, Vision, Life & Disability insurances - for you and your family!
  • Retirement Plans: Babel Street offers both a Traditional and Roth 401(K) with a very competitive match.
  • Unlimited Flexible Leave: We trust our employees to manage their own time and balance their personal and work lives.
  • Holidays: Babel Street provides employees with 12 paid Federal Holidays
  • Tuition Reimbursement: We are committed to investing in our employees. One way we do that is with our Tuition Reimbursement Program for continuing education.                 

Babel Street is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. Further, Babel Street will not discriminate against applicants for inquiring about, discussing or disclosing their pay or, in certain circumstances, the pay of their coworker, Pay Transparency Nondiscrimination. In addition, Babel Street's policy is to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works. Upon request, we will provide you with more information about such accommodations.