Knowledge Graph & Backend Engineering Manager

Accenture

Overland Park (KS)

Hybrid

USD 150,000 - 210,000

Full time

3 days ago

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Job summary

Accenture is seeking a Back-End Engineer to design, build, and operate data infrastructure powering an AI-driven knowledge platform. You will architect APIs, build data pipelines, manage multi-modal databases, and ensure reliability and performance for enterprise-scale services.

You will work across the back-end stack, designing relational schemas, tuning graph queries, and deploying vector stores while applying semantic technology expertise to deliver scalable data solutions for application

Qualifications

Minimum 5 years experience in Knowledge Graph data hydration and ontology-based data mapping, including strong understanding of RDF, SPARQL, and semantic technologies.
Minimum 5 years experience with R2RML or similar mapping frameworks for transforming relational data into graph models.
Minimum 5 years experience with graph databases (e.g., StarDog, GraphWise, Neo4J), along with Elasticsearch/OpenSearch, including strong SQL proficiency.
Minimum 5 years hands-on experience with relational databases (e.g., PostgreSQL, MySQL, or SQL Server), including schema design, indexing, and query optimization.
Minimum 5 years experience deploying and operating vector databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) in production environments.
Minimum 5 years Proficiency in Python or Java for automation, integration, and service development and experience designing and documenting REST APIs for internal consumers.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)

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.