We are looking for Forward Deployed Engineer (FDE) - Data Architect, who will directly engage with cross-functional business, AI, and engineering teams to design and deploy scalable, production-grade Knowledge Graph solutions and robust Data Governance frameworks. This role will bridge complex semantic data modeling with real-world enterprise applications to drive contextual AI and unified metadata management.
Responsibilities- Knowledge Graph Engineering: Architect, design, and deploy enterprise Knowledge Graph solutions using Property Graphs and semantic technologies.
- Semantic Modeling: Build, maintain, and extend ontologies, taxonomies, metadata models, and enterprise knowledge structures tailored to client domain requirements.
- Data Governance & Metadata Management: Establish integrated data governance frameworks, including end-to-end data lineage, automated metadata management, business glossaries, data stewardship workflows, and data quality standards.
- Cross-Functional Collaboration: Partner closely with business stakeholders, AI/ML engineers, and data platforms teams to translate complex business domains into scalable semantic models.
- Technical Integration: Integrate knowledge graphs into broader enterprise data fabrics, data meshes, and AI systems (e.g., Graph-RAG, LLM context engines).
- Delivery & Leadership: Act as a technical authority on customer deployments, translating ambiguous client needs into clean, scalable data architecture blueprints.
Requirements- Knowledge Graphs: Proven experience designing and delivering enterprise Knowledge Graph systems in production.
- Semantic Foundations: Deep expertise in ontology modeling, taxonomy development, metadata architectures, and Property Graph models (e.g., Neo4j, Amazon Neptune, Cosmos DB).
- Governance Expertise: Strong track record in enterprise Data Architecture and Governance, including hands-on experience with metadata lineage, data quality platforms, stewardship frameworks, and enterprise business glossaries.
- Cross-Domain Execution: Demonstrated ability to bridge technical AI/engineering teams and non-technical business stakeholders to drive consensus on data models.
- Technical Stack: Proficiency with graph query languages (Cypher, Gremlin, or SPARQL), graph databases, and modern data stack integration pattern (ETL/ELT pipelines, APIs, and cloud platforms).
BenefitsSignificant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
