Data Ontologist

Are you a Data Ontologist working at a Large Financial Institution and being told by your leadership that you are too hands on or detail oriented or think and work like a start-up?

Imagine working at Intellibus to engineer platforms that impact billions of lives around the world. With your passion and focus we will accomplish great things together!

We are looking forward to you joining our Platform Engineering Team.

Our Platform Engineering Team is working to solve the Multiplicity Problem. We are trusted by some of the most reputable and established FinTech Firms. Recently, our team has spearheaded the Conversion & Go Live of apps which support the backbone of the Financial Trading Industry.

We are looking for Engineers who can
Create and maintain ontologies, taxonomies, and data models that structure information in a meaningful way, making it easy to access, integrate, and interpret across systems.
Implement semantic technologies such as RDF, OWL, and SPARQL to build knowledge graphs and enhance data interoperability.
Collaborate with business stakeholders and domain experts to ensure that all data is standardized and semantically consistent across systems.
Define and manage metadata to enhance data usability, supporting projects like search optimization, data integration, and machine learning models.
Develop ontologies and data models that support artificial intelligence (AI) and machine learning (ML) algorithms, ensuring accurate data representation for predictive analytics and automated decision-making.
Experience working with enterprise data management systems.
Familiarity with cloud-based semantic tools (e.g., AWS Neptune, Google Knowledge Graph).
Previous experience working within the financial services or consulting industry is a must
Skills and Qualifications
Education: Bachelor’s or Master’s degree in Information Science, Computer Science, Data Science, or related fields.
Experience: 8+ years of experience in data management, with at least 5+ years focused on ontology design, semantic web technologies, or knowledge representation.
Proficiency in ontology languages such as RDF, OWL, and query languages like SPARQL.
Experience with knowledge graph technologies (e.g., Neo4j, GraphDB, or Stardog).
Strong understanding of metadata management, data integration, and data interoperability.
Familiarity with machine learning and AI concepts.
Advanced use of MS Office, especially Excel and PowerPoint for presenting complex data structures.
Analytical Skills: Excellent problem-solving abilities and experience in conceptualizing and developing structured data solutions.
Communication Skills: Ability to communicate complex technical concepts to both technical and non-technical stakeholders.
Collaboration: Strong team player with the ability to collaborate across cross-functional teams, including data engineers, scientists, and business leaders.
We work closely with
AWS S3
Database Design
Data Integration
Jenkins
Splunk / ELK
Amazon VPC
Datadog New Relic / Wavefront
PCF
CI/CD
ECS
Lambda

Our Process
Schedule a 15 min Video Call with someone from our Team
4 Proctored GQ Tests (< 2 hours)
30-45 min Final Video Interview
Receive Job Offer

If you are interested in reaching out to us, please apply and our team will contact you within the hour.