What are the responsibilities and job description for the Knowledge Graph Engineer position at Intuitive.ai?
About us:
Intuitive.AI is one of the fastest-growing (INC 5000, CRN) Cloud & SDx solution and services companies supporting enterprise customers on a global scale. Intuitive is an "Engineering Company" delivering measurable value and key business outcomes.
Intuitive Superpowers:
- DataOps & AI/ML
- Cloud Native, AppSecOps, DevSecOps
- Cloud Migration & Transformation
- Cloud FinOps
- Cybersecurity (App/Data/Infra) & GRC
- SDx & Digital Workspace
We are proud to partner with some of the world's leading enterprises and serve 200 customers across different industry verticals. We have achieved many milestones along the way, including being recognized as a top-10 fast-growth 150 IT company in the Americas by CRN in 2022 and being named one of America's fastest-growing private companies by INC 5000 in 2022. That’s not all! Even CIO Review awarded us as the Most Promising Cloud Migration Company and Artificial Intelligence Solutions Provider in 2022.
About the job:
Title – Principal Data Engineer — Ontology
Start date: Immediate
Position Type: Full Time
Location: Hybrid (Dallas, TX; Malvern, PA; Charlotte, NC)
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.
Open Standards Orientation
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.
Engineering Mindset
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.
Change Management Awareness
Sensitivity to how new standards, APIs, and information structures are adopted within organizations. Appreciation for governance, ownership, and the realities of evolving legacy practices.