Ontology FDO

Job Summary:
Client is seeking a Graph Engineer / Ontologist to support the design, development, and implementation of enterprise knowledge graphs and semantic data solutions. The role will involve translating complex business and regulatory concepts into robust ontologies, graph models, and reusable data products that improve data discovery, integration, governance, and analytics across the organization.

The successful candidate will work closely with data engineers, software developers, architects, subject-matter experts, risk and compliance teams, and business stakeholders to create scalable graph-based solutions in a highly regulated financial-services environment.

Key Responsibilities:

·  Design and develop knowledge graphs, ontologies, taxonomies, and semantic information models.

·  Translate business, operational, financial, and regulatory requirements into formal graph and ontology specifications.

·  Define entities, relationships, attributes, constraints, classifications, and semantic rules.

·  Develop graph data models using appropriate standards and technologies, such as:

- RDF and RDF-star
- OWL and SKOS
- SHACL
- SPARQL
- Property-graph models and Cypher

·  Build data-mapping and ingestion processes from relational databases, APIs, documents, and other enterprise data sources.

·  Integrate graph solutions with data platforms, data catalogues, data-quality tools, and analytical applications.

·  Establish ontology governance, including versioning, change management, documentation, reuse, and lifecycle management.

·  Collaborate with domain experts to create consistent definitions of financial, legal, organizational, risk, and regulatory concepts.

·  Develop validation rules and quality controls to ensure graph data is accurate, complete, consistent, and traceable.

·  Write and optimize graph queries and data-transformation pipelines.

·  Support graph-based search, lineage, impact analysis, entity resolution, data discovery, and analytics use cases.

·  Contribute to technical design reviews, engineering standards, testing, deployment, and operational support.

·  Produce clear technical documentation, modelling guidelines, and user documentation.

·  Promote best practices in semantic modelling, metadata management, data governance, and responsible use of data.

·  Stay informed about developments in knowledge graphs, semantic technologies, AI, data architecture, and financial-services regulation.

Required Skills and Experience:

·  Experience in graph engineering, ontology development, semantic modelling, knowledge representation, or a closely related discipline.

·  Strong understanding of data modelling principles and information architecture.

·  Practical experience with one or more of:

- RDF, OWL, SKOS, SHACL, SPARQL
- Neo4j, Amazon Neptune, Stardog, GraphDB, AllegroGraph, or similar graph platforms
- Cypher or other graph-query languages

·  Experience with data integration, ETL/ELT pipelines, APIs, relational databases, and structured or semi-structured data.

·  Proficiency in at least one programming language, such as Python, Java, or Scala.

·  Ability to analyze complex requirements and represent them in clear, maintainable conceptual and logical models.

·  Understanding of ontology design principles, including reuse, modularity, alignment, extensibility, and governance.

·  Experience working with cross-functional stakeholders and explaining technical concepts to non-technical audiences.

·  Strong problem-solving, analytical, communication, and documentation skills.

·  A disciplined approach to security, data quality, controls, and operational risk.

Desirable Skills:

- Experience in banking, capital markets, financial services, risk, compliance, or regulatory data.
- Knowledge of financial-industry standards and reference models, such as FIBO, ISO 20022, LEI, or other recognized data standards.
- Experience with data lineage, metadata management, master data management, data catalogues, or data governance.
- Familiarity with cloud platforms, containerisation, CI/CD, and infrastructure-as-code.
- Experience with entity resolution, natural-language processing, machine learning, or graph-based AI applications.
- Knowledge of software engineering practices, automated testing, code review, and agile delivery.
- Experience supporting enterprise-scale platforms with high availability, security, and performance requirements.
- Relevant degree or equivalent experience in computer science, information systems, data science, mathematics, engineering, or a related field.

S​ YNECHRON’S DIVERSITY & INCLUSION STATEMENT

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.

All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law .

Candidate Application Notice