Enterprise Knowledge Graph Developer

Job description

Founded in 1994 and headquartered in Switzerland, ERNI is a leading Software Development company with over 800 employees worldwide. Specializing in IT and software engineering, we drive innovation in process and technology. Our first service center in Asia Pacific, located in Metro Manila (Mandaluyong), supports clients across Europe, APAC, the Philippines, and the USA. As we continue to grow, we're looking for passionate and motivated individuals to join our team. Why ERNI is the Perfect Place for You: 🏡 • International Exposure: Work with global clients on cutting-edge projects. • Inclusive Culture: Thrive in a collaborative and diverse work environment. • Career Development: Enjoy continuous learning and professional growth opportunities. 🤩Perks and Benefits: • Career Stability: Enjoy a stable career path with ample project opportunities. • Immediate Coverage: Private HMO and insurance benefits from day one. • Jubilee Celebration: A 5-year milestone includes a complimentary trip to any European ERNI sites . • Comprehensive Benefits: Government-mandated benefits including 13th-month pay. • Skill Enhancement: Access free training and certifications. • Baby Basket: To welcome your newborn to the ERNI family. • Fruit Basket: Boost of vitamins during hospitalization. • Office Perks: Enjoy free snacks and coffee. 🔐Growth and Opportunities: • Free Training: Advance your skills through technical and non-technical training. • Challenging Projects: Engage in complex software projects across MedTech, Industry, Finance, and Transportation. • Supportive Environment: Benefit from a team dedicated to guiding and supporting your success. • Recognition and Advancement: Receive acknowledgment for your efforts and opportunities for promotion. • Open Communication: Experience transparency and value your input in our culture. ⏱Flexibility: • Hybrid Work Setup: Balance remote and in-person work for better work-life integration. 🎉Events: • Connect and Celebrate: Participate in a variety of events including leisure, summer, family, social, and year-end gatherings. 👋What are our wishes: TECHNICAL SKILLS PROFILE Core Technologies : RDF/OWL/SHACL | SPARQL / Cypher | Neo4j / Amazon Neptune | Stardog / GraphDB | Python (rdflib, NetworkX) | Java / Scala | Apache Spark / Kafka | GraphRAG / LangChain | Entity Resolution | CI/CD & Docker Required Minimum Qualifications Education : Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, Knowledge Engineering, or a highly technical equivalent field. Experience : Minimum of 4–6 years in core data engineering or software development, with at least 2–3 years of dedicated hands-on experience designing and operating production-grade graph databases and semantic web stacks. Preferred Technical Competencies Deep literacy in W3C semantic web standards (RDF, RDFS, OWL, SPARQL, SHACL). Proven mastery over either Property Graph systems (Neo4j, Memgraph) or RDF Triplestores/Quadstores (Stardog, GraphDB, Ontotext, AWS Neptune). Proficiency in backend development using Python or Java/Scala, alongside enterprise pipeline frameworks like Apache Spark, Apache Kafka, or NiFi. Direct exposure to building semantic contexts for LLMs, knowledge distillation, or vector-graph hybrid index stores. KEY COMPETENCIES & SOFT SKILLS Abstract & Systems Thinking: Ability to step back from tabular flat file systems and naturally conceptualize multi-dimensional data networks, hyper-graphs, and structural relationships. Cross-Functional Translation: Exceptional capacity to talk to non-technical business leaders to distill ambiguous operational terminology into structured, definitive business glossaries and taxonomies. Rigorous Data Governance Mindset : A firm commitment to programmatic data quality validation, automated contract verification, and enforcing strictly typed data schemas at ingestion borders. 💼How can you contribute to the team? ROLE OVERVIEW The Enterprise Knowledge Graph Developer is a specialized data engineering and architecture role responsible for designing, building, and scaling the organization’s unified semantic data layer. This position bridges the gap between disparate enterprise data silos, relational systems, and downstream intelligent applications (including GenAI, LLM agents, and semantic search engine platforms) by structuring organizational data into a high-performance, interconnected graph network. POSITION METADATA Job Level: Senior / Lead Specialist Department : Enterprise Data & AI Architecture Reports To : Director of Data Engineering / Principal Architect Core Focus: Ontology Design, Graph ETL, Semantic Linking CORE RESPONSIBILITIES Ontological Modeling & Schema Design Collaborate with domain experts, business analysts, and data stewards to conceptualize, define, and build scalable enterprise ontologies and taxonomies. Translate complex business rules and relationship constraints into formal semantic models using W3C standards (OWL, RDF, SHACL, SKOS) Ensure semantic models remain highly modular, extensible, and integrated across multi-departmental business units. Graph Pipeline Infrastructure & Integration (Graph ETL) Design, implement, and maintain scalable data pipelines (batch and real-time) to ingest, extract, transform, and map structured, semi-structured, and unstructured data into the centralized knowledge graph Incorporate Entity Resolution, Record Linkage, and disambiguation algorithms to dynamically reconcile duplicate identities across disparate transactional systems Construct robust mappings from relational DBs, data lakes, and document stores using declarative mapping languages (e.g., R2RML, RML). Graph Database Management & Optimization Administer, tune, and maintain distributed native graph databases (GraphDB, Neo4j, Neptune, AnzoGraph, or Stardog) Write, test, and perform fine-grained optimization of complex, multi-hop semantic queries using SPARQL, Cypher, or Gremlin Implement partitioning, sharding, caching, and index strategies to guarantee sub-second query execution for real-time upstream enterprise systems. GenAI, LLM, and Agentic AI Enablement Integrate the Enterprise Knowledge Graph with Large Language Model (LLM) frameworks to implement high-precision Graph Retrieval-Augmented Generation (GraphRAG). Expose semantic layers via secure GraphQL or RESTful APIs to feed multi-agent autonomous frameworks, ensuring deterministic and highly contextual reasoning paths Utilize graph analytics, embeddings, and Graph Neural Networks (GNNs) to drive advanced inferencing and recommendation capabilities.