Define and own the Graph Technology strategy within the Cyber Data ecosystem, aligning with enterprise data and security architectures
Design large‑scale graph data models representing complex cyber relationships such as assets, identities, vulnerabilities, configurations, threats, and attack paths
Establish reference architectures and reusable design patterns for graph platforms supporting analytics, traversal, correlation, and inference
Evaluate, recommend, and influence graph platforms and tooling (e.g., property graphs, RDF, cloud‑native graph services) through build vs. buy assessments
Lead hands‑on engineering efforts to implement scalable graph solutions, including rapid proofs of concept (POCs) for emerging threat scenarios and analytics use cases
Partner with data and platform engineering teams to integrate graph data stores with existing data pipelines, analytics platforms, and observability tooling
Drive performance, scalability, and reliability optimizations for large, high‑volume graph datasets
Ensure graph solutions meet enterprise requirements for security, resiliency, data quality, and operational excellence
Enable advanced analytics use cases such as attack path analysis, blast radius modeling, threat correlation, entity resolution, and risk prioritization using graph technologies
Collaborate with Cyber domain leaders, detection engineers, and data scientists to translate threat vectors into actionable graph‑based insights
Influence how Cyber teams consume graph insights to detect threats earlier, reduce mean time to respond (MTTR), and improve proactive defense
Serve as the go‑to technical authority for graph technologies across Cyber Engineering and Data communities
Guide multiple engineering teams through design reviews, architectural decisions, and technical problem‑solving without direct managerial authority
Mentor senior and staff engineers, raising the technical bar across the organization
Clearly articulate technical concepts, trade‑offs, and architecture decisions to senior engineering leaders and executive stakeholders

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
7+ years of hands-on software or data engineering experience, with significant depth in distributed systems or data platforms
3+ years of deep expertise designing and implementing graph technologies at scale
Proven experience operating as a technical lead or principal-level engineer influencing architecture across multiple teams
Deep expertise in graph databases and frameworks (e.g., Neo4j, TigerGraph, JanusGraph, Amazon Neptune, Azure Cosmos DB Graph, or equivalent)
Strong experience designing complex graph data models for large, highly connected datasets
Familiarity with graph query languages (Cypher, Gremlin, SPARQL)
Experience building graph-powered analytics for cybersecurity, risk, fraud, or complex domain modeling
Strong knowledge of cloud-native architectures (Azure, AWS, GCP) and data platform integration
Experience integrating graph data with streaming, batch, and analytics platforms
Solid grounding in secure software development lifecycle (SSDLC) and enterprise risk expectations
Exceptional ability to influence without authority and operate effectively in large, matrixed organizations
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field; advanced certifications are a plus

Benefits

Health benefits
401(k) Plan
Paid time off
Disability benefits
Life insurance, critical illness insurance, and accident insurance
Parental leave
Critical caregiving leave
Discounts and savings
Commuter benefits
Tuition reimbursement
Scholarships for dependent children
Adoption reimbursement

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

graph technologiesgraph databasesdistributed systemsdata platformsgraph data modelsgraph query languagescloud-native architecturessecure software development lifecycleanalyticsdata integration

Soft Skills

influence without authoritytechnical leadershipmentoringcollaborationcommunicationproblem-solvingarchitectural decision-makingstakeholder engagementteam guidanceoperational excellence

Certifications

Bachelor’s degree in Computer ScienceMaster’s degree in Engineeringadvanced certifications