Knowledge graph Engineer

Intuitive.ai; United States; Hybrid; USD 90,000 - 120,000; Full time

Job summary

Intuitive.ai is seeking a Full Time position with the flexibility to work remotely or in a hybrid model based on proximity to Dallas, TX; PA; or Charlotte, NC. The job requires a strong foundation in data engineering, emphasizing data governance and the ability to model effective information systems.

Prospective candidates must demonstrate excellent communication skills, the ability to learn new technologies, and perform in ambiguous environments, showcasing a firm grasp on both technical and business outcomes.

Qualifications

• Strong data engineering background with understanding of data governance and structuring.
• Experience with AWS S3-based data lakes and managed databases.
• Ability to organize business concepts and map real data to models.

Responsibilities

• Explain complex data concepts in plain language.
• Integrate conceptual models into real systems.
• Work effectively in ambiguity and adapt to new ideas.

Skills

DataOps & AI/ML

Cybersecurity (App/Data/Infra)

SDx & Digital Workspace

Tools

AWS data platforms

GraphQL APIs

REST APIs

Job description

About the job

Position Type: Full Time

Location: Remote or Hybrid if local to Dallas, TX; PA; Charlotte, NC

Superpowers

• DataOps & AI/ML
• Cybersecurity (App/Data/Infra) & GRC
• SDx & Digital Workspace

Must Have

These are the capabilities we cannot compromise on. They reflect information discipline and engineering maturity rather than tool familiarity.

Data Engineering and Information Management Fundamentals

Strong data engineering background with a clear understanding of how data is structured, governed, versioned, and moved across systems. Experience designing durable information models that outlive any single source or implementation.

Required experience includes AWS data platforms, specifically S3‑based data lakes and AWS‑managed databases. Familiarity with treating data as a long‑lived information asset is essential.

Information Modeling

Ability to organize business concepts clearly, separate meaning from storage, and map real data to conceptual models. Comfort aligning internal models to shared or external standards rather than optimizing only for local schemas.

Abstract Thinking and Adaptability

Comfort working in ambiguity and reasoning from first principles. Ability to learn new modeling approaches, technologies, and standards quickly, adjust assumptions, and refine models as understanding deepens.

Experience working with open standards in any technology domain, including data formats, APIs, identifiers, or metadata specifications. This may include REST or GraphQL APIs, schema standards, or industry data models. Demonstrated ability to read standards, understand intent, and apply them pragmatically even when the standard is new.

Practical experience integrating conceptual models into real systems. This includes mapping models to data layers, exposing or consuming APIs such as GraphQL, supporting mock or lightweight integrations, and using version control and basic DevOps practices with discipline.

Communication

Ability to explain complex information and data concepts in plain language and connect technical decisions to business outcomes. Clear written and verbal communication is essential.

Nice to Have

These skills accelerate impact but can be learned by the right engineer.

Ontology and Knowledge Graph Technologies

Familiarity with ontology and semantic standards such as SKOS, RDF, OWL, and SHACL, or hands‑on experience with knowledge graph technologies and graph databases. Prior depth is helpful but not required if the engineer demonstrates strong information modeling instincts and learning ability.

Asset Management Domain Knowledge

Understanding of investment products, asset management concepts, and common industry schemas. Domain exposure helps, but strong modeling and engineering skills can bridge gaps.

Sensitivity to how new standards, APIs, and information structures are adopted within organizations. Appreciation for governance, ownership, and the realities of evolving legacy practices.