Senior Data Architect - Knowledge Engineering - Accenture

OverviewRevise detenidamente toda la documentación de la solicitud antes de hacer clic en el botón de solicitar al final de esta descripción.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 / BeneficiosPrivate medical, life, and accident insuranceMedical services and wellbeing programsFlexible benefits programLearning and career development pathsSustainability initiatives and Accenture Foundation participationProfit-sharing programsResponsabilidadesLead development of Knowledge Graph solutions within a program scopeDesign, build, and deploy AI and semantic solutions ensuring cohesive integrationCollaborate with project leaders and clients to create graph-powered data offeringsDevelop client relationships and earn stakeholder trustArticulate business value of semantic layer solutions to clientsContribute to Accenture sales and pre-sales effortsProvide thought leadership on technology trends, opportunities, and risksRequisitos principalesBachelor’s degree or equivalent, plus at least 3 of the following: production deployments in data/AI/Software engineeringStrong knowledge of relational DBs, object stores, graph databases (Stardog, Neo4J, Amazon Neptune, Azure Cosmos DB, Allegro Graph)Proficiency in SPARQL and/or Gremlin/CypherHands-on with at least two graph DB platforms across RDF and property graph paradigmsExperience integrating graph layers with enterprise governance platforms (Collibra, Informatica, Apache Atlas, Alation)Cloud data architecture on AWS and/or xqysrnh AzureStrong Python skills with TensorFlow/PyTorch, ETL tools (NiFi, Airflow), and advanced SQLExperience in ontology management, KG curation, and KG-based ML modelsEnd-to-end data pipeline design for AI apps (incl. LLMs)Fluency in Spanish and good EnglishStrong communication and stakeholder managementTeam leadership and mentorshipCross-functional collaboration across engineering, research, and productKnowledge Graph design and ontology managementGraph databases and query languages (SPARQL, Gremlin, Cypher)RDF triple store and property graph paradigms

You’ll collaborate with clients and stakeholders to articulate value, drive end-to-end delivery, and push innovation through cutting-edge graph methods.Compensaciones / BeneficiosPrivate medical, life, and accident insuranceMedical services and wellbeing programsFlexible benefits programLearning and career development pathsSustainability initiatives and Accenture Foundation participationProfit-sharing programsResponsabilidadesLead development of Knowledge Graph solutions within a program scopeDesign, build, and deploy AI and semantic solutions ensuring cohesive integrationCollaborate with project leaders and clients to create graph-powered data offeringsDevelop client relationships and earn stakeholder trustArticulate business value of semantic layer solutions to clientsContribute to Accenture sales and pre-sales effortsProvide thought leadership on technology trends, opportunities, and risksRequisitos principalesBachelor’s degree or equivalent, plus at least 3 of the following: production deployments in data/AI/Software engineeringStrong knowledge of relational DBs, object stores, graph databases (Stardog, Neo4J, Amazon Neptune, Azure Cosmos DB, Allegro Graph)Proficiency in SPARQL and/or Gremlin/CypherHands-on with at least two graph DB platforms across RDF and property graph paradigmsExperience integrating graph layers with enterprise governance platforms (Collibra, Informatica, Apache Atlas, Alation)Cloud data architecture on AWS and/or xqysrnh AzureStrong Python skills with TensorFlow/PyTorch, ETL tools (NiFi, Airflow), and advanced SQLExperience in ontology management, KG curation, and KG-based ML modelsEnd-to-end data pipeline design for AI apps (incl. LLMs)Fluency in Spanish and good EnglishStrong communication and stakeholder managementTeam leadership and mentorshipCross-functional collaboration across engineering, research, and productKnowledge Graph design and ontology managementGraph databases and query languages (SPARQL, Gremlin, Cypher)RDF triple store and property graph paradigms