Knowledge Engineer Manager - Back-end Engineer

Accenture•St. Louis, MO

6d•$80,400 - $293,800•Hybrid

About The Position

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.

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, 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

Nice To Haves

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.
Design and build user-facing applications and dashboards that surface Knowledge Graph data to end users.
Develop and maintain 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.

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.