Back-End Engineer

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., * 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.

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, Google Cloud Platform

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%

Here’s What You Need:

• 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

Bonus Points if You Have:

• 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 Google Cloud Platform).
• 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.

Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here

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