Knowledge Graph Engineer

Location: Hybrid role for the US PA Area, Dallas, Charlotte NC, or NJ (with travel to PA as needed).

About the Role

We are looking for an experienced data engineer with ontology and knowledge graph expertise. The ideal candidate will demonstrate strong information discipline and engineering maturity, with a focus on durable data modeling and open standards.

Must-Have Capabilities

• 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 with AWS data platforms, specifically S3-based data lakes and AWS-managed databases.
  • Familiarity with treating data as a long-lived information asset.
• 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 (e.g., REST, GraphQL, 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, including mapping models to data layers and exposing or consuming APIs such as GraphQL.
  • Experience 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 Skills

• 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 is helpful but not required.

• Change Management Awareness

  Sensitivity to how new standards, APIs, and information structures are adopted within organizations, including governance, ownership, and evolving legacy practices.