Knowledge Engineer Manager - Back-end Engineer

About the role

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.

This role is hybrid and may require 20-80% travel to client locations.

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.

Requirements

• 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, GraphDB, 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.
• 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).

Preferred Skills

• Familiarity with containerization tools (Docker, Kubernetes).
• Knowledge of data ingestion pipelines, ETL/integration, and enterprise system integration.
• Understanding of PII/PHI handling, data anonymization, and data governance.
• Experience designing and building user-facing applications and dashboards that surface Knowledge Graph data to end users.
• Experience developing and maintaining REST and GraphQL APIs bridging graph backends and frontend clients.
• Experience with graph visualization libraries (D3.js, Cytoscape.js).
• Familiarity with a BFF (Backend for Frontend) or API gateway pattern.
• Experience with AI agent-driven pipelines or RAG architectures.
• Understanding of Federated Knowledge Graph architectures.
• Experience working across Development, Test, UAT, and Production environments.
• Cloud platform experience (AWS Neptune, Azure Cosmos DB, or GCP).
• Experience with event-driven architectures and message queuing (Kafka, RabbitMQ).
• Familiarity with infrastructure-as-code tools (Terraform, Helm).
• Experience with database replication, partitioning, and high-availability patterns for relational systems.
• Familiarity with vector embedding pipelines and strategies for chunking, re-ranking, and retrieval optimization.

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

Pay

Compensation varies by location. Annual salary ranges for this role are:

• California: $94,400 to $293,800
• Cleveland: $87,400 to $235,000
• Colorado: $94,400 to $253,800
• District of Columbia: $100,500 to $270,300
• Illinois: $87,400 to $253,800
• Maine: $80,400 to $216,200
• Maryland: $94,400 to $253,800
• Massachusetts: $94,400 to $270,300
• Minnesota: $94,400 to $253,800
• New York: $87,400 to $293,800
• New Jersey: $100,500 to $293,800
• Virginia: $87,400 to $270,300
• Washington: $100,500 to $270,300

Benefits

Accenture offers a market-competitive suite of benefits including:

• Medical, dental, vision, life, and long-term disability coverage
• 401(k) plan
• Bonus opportunities
• Paid holidays and paid time off

For more details, visit U.S. Employee Benefits | Accenture.