Senior Data Architect - Knowledge Engineering - Accenture

Overview As a Knowledge Graph Engineer in Accenture’s Data & AI practice, you will lead the architecture and delivery of semantic data solutions across industries, spanning AWS and Azure.You will shape domain models, ontologies, and scalable KG solutions, guiding teams from design to production.You’ll collaborate with clients and stakeholders to articulate value, drive end-to-end delivery, and push innovation through cutting-edge graph methods.Compensaciones / Beneficios Private medical, life, and accident insurance Medical services and wellbeing programs Flexible benefits program Learning and career development paths Sustainability initiatives and Accenture Foundation participation Profit-sharing programs Responsabilidades Lead development of Knowledge Graph solutions within a program scope Design, build, and deploy AI and semantic solutions ensuring cohesive integration Collaborate with project leaders and clients to create graph-powered data offerings Develop client relationships and earn stakeholder trust Articulate business value of semantic layer solutions to clients Contribute to Accenture sales and pre-sales efforts Provide thought leadership on technology trends, opportunities, and risks Requisitos principales Bachelor’s degree or equivalent, plus at least 3 of the following: production deployments in data/AI/Software engineering Strong knowledge of relational DBs, object stores, graph databases (Stardog, Neo4J, Amazon Neptune, Azure Cosmos DB, Allegro Graph) Proficiency in SPARQL and/or Gremlin/Cypher Hands-on with at least two graph DB platforms across RDF and property graph paradigms Experience integrating graph layers with enterprise governance platforms (Collibra, Informatica, Apache Atlas, Alation) Cloud data architecture on AWS and/or Azure Strong Python skills with TensorFlow/PyTorch, ETL tools (NiFi, Airflow), and advanced SQL Experience in ontology management, KG curation, and KG-based ML models End-to-end data pipeline design for AI apps (incl. LLMs) Fluency in Spanish and good English Strong communication and stakeholder management Team leadership and mentorship Cross-functional collaboration across engineering, research, and product Knowledge Graph design and ontology management Graph databases and query languages (SPARQL, Gremlin, Cypher) RDF triple store and property graph paradigms

Compensaciones / Beneficios Private medical, life, and accident insurance Medical services and wellbeing programs Flexible benefits program Learning and career development paths Sustainability initiatives and Accenture Foundation participation Profit-sharing programs Responsabilidades Lead development of Knowledge Graph solutions within a program scope Design, build, and deploy AI and semantic solutions ensuring cohesive integration Collaborate with project leaders and clients to create graph-powered data offerings Develop client relationships and earn stakeholder trust Articulate business value of semantic layer solutions to clients Contribute to Accenture sales and pre-sales efforts Provide thought leadership on technology trends, opportunities, and risks Requisitos principales Bachelor’s degree or equivalent, plus at least 3 of the following: production deployments in data/AI/Software engineering Strong knowledge of relational DBs, object stores, graph databases (Stardog, Neo4J, Amazon Neptune, Azure Cosmos DB, Allegro Graph) Proficiency in SPARQL and/or Gremlin/Cypher Hands-on with at least two graph DB platforms across RDF and property graph paradigms Experience integrating graph layers with enterprise governance platforms (Collibra, Informatica, Apache Atlas, Alation) Cloud data architecture on AWS and/or Azure Strong Python skills with TensorFlow/PyTorch, ETL tools (NiFi, Airflow), and advanced SQL Experience in ontology management, KG curation, and KG-based ML models End-to-end data pipeline design for AI apps (incl. LLMs) Fluency in Spanish and good English Strong communication and stakeholder management Team leadership and mentorship Cross-functional collaboration across engineering, research, and product Knowledge Graph design and ontology management Graph databases and query languages (SPARQL, Gremlin, Cypher) RDF triple store and property graph paradigms