Knowledge Engineer Manager - Back-end Engineer

We Are:

Accenture is a global professional services and solutions company that helps leading organizations reinvent with digital, cloud, data, and AI capabilities, drawing on its large global workforce, industry expertise, and ecosystem partnerships to create 360° value.

By combining hands-on engineering with deep industry context, we help organizations build their digital core, modernize operations, unlock value from data and AI, and deliver tangible business outcomes at speed and scale.

Role Summary:

We are looking for a Back-End Engineer to design, build, and operate the data infrastructure and services that power our AI-driven knowledge platform. You will work across the full back-end stack - architecting APIs, building data pipelines, managing multi-modal database systems, and owning the reliability and performance of the services that application and product teams depend on. You bring strong engineering fundamentals and are equally comfortable designing a relational schema, tuning a graph query, standing up a vector store, or shipping a production-grade REST API. Knowledge graph and semantic technology experience is central to this role, but the work extends across the broader data and service layer: ingestion, transformation, storage, retrieval, and delivery at enterprise scale.

You Are:

We are looking for a Back-End Engineer to design, build, and operate the data infrastructure and services that power our AI-driven knowledge platform. You will work across the full back-end stack - architecting APIs, building data pipelines, managing multi-modal database systems, and owning the reliability and performance of the services that application and product teams depend on. You bring strong engineering fundamentals and are equally comfortable designing a relational schema, tuning a graph query, standing up a vector store, or shipping a production-grade REST API. Knowledge graph and semantic technology experience is central to this role, but the work extends across the broader data and service layer: ingestion, transformation, storage, retrieval, and delivery at enterprise scale.

Key Responsibilities:

• Hydrate structured and semi-structured data into Knowledge Graphs by mapping source data to ontology models.

• Develop data mapping and transformation workflows using R2RML or similar technologies.

• Write and optimize SPARQL queries for graph loading, validation, and retrieval.

• Build and maintain data ingestion pipelines and integrate data from enterprise systems.

• Design and optimize relational database schemas and queries to support efficient graph hydration and ETL workflows.

• Deploy, configure, and maintain vector database infrastructure for embedding storage, indexing, and semantic retrieval at scale.

• Design and maintain scalable graph query APIs consumed by internal application and product teams.

• Performance-tune graph database queries, indexing strategies, and data access patterns.

• Own containerization, deployment, and monitoring of graph services in cloud environments.

• Ensure data quality, ontology alignment, and secure handling of sensitive data (PII/PHI).

• Collaborate with ontologists, architects, and application teams to support Knowledge Graph implementations.

Preferred Technology Stack:

Semantic technologies: RDF, OWL, SKOS, RDFS

Query languages: SPARQL

Mapping technologies: R2RML, CSVW, SHACL (preferred)

Graph databases: GraphDB, Stardog, Neo4j, Amazon Neptune

Relational databases: PostgreSQL, MySQL, SQL Server

Vector stores: Pinecone, Weaviate, Milvus, Qdrant

Search: Elasticsearch / OpenSearch

Programming: Python, Java (preferred)

Data formats: SQL, JSON, XML, CSV

Integration: REST APIs, ETL tools, Apache NiFi, Airflow (preferred)

Infrastructure: Docker, Kubernetes, Helm

Cloud: AWS Neptune, Azure Cosmos DB, GCP

Messaging: Kafka, RabbitMQ

Version control: Git

This role is hybrid in nature and will require time in office and traveling to client locations. Travel can be between 20-80%