Knowledge Engineer Manager - Back-end Engineer

Information Technology Senior Management Forum; Seattle (WA); Hybrid; USD 162,000 - 270,000; Full time

Job summary

Accenture is seeking a Back-End Engineer to design, build, and operate data infrastructure powering an AI-driven knowledge platform in a hybrid role. You will architect APIs, build data pipelines, and own reliability across the back-end stack for enterprise-scale systems.

You will work with ontology-driven data, RDF/SPARQL, and graph databases, contributing to ingestion, transformation, storage, and retrieval. Role involves travel to client locations as needed.

Qualifications

• Minimum 5 years experience in Knowledge Graph data hydration and ontology-based data mapping, including RDF, SPARQL, and semantic technologies.
• Minimum 5 years experience with mapping frameworks like R2RML for transforming relational data into graph models.
• Minimum 5 years experience with graph databases (e.g., Neo4J, StarDog, GraphWise) and Elasticsearch/OpenSearch, plus strong SQL skills.
• Minimum 5 years hands-on experience with relational databases (PostgreSQL, MySQL, SQL Server) and schema design.
• Minimum 5 years deploying vector databases (Pinecone, Weaviate, Milvus, Qdrant) in production.
• Minimum 5 years Proficiency in Python or Java and designing REST APIs for internal use.

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 graph hydration and ETL workflows.
• Deploy, configure, and maintain vector database infrastructure for embedding storage and retrieval at scale.
• Design and maintain scalable graph query APIs used by internal 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 on Knowledge Graph implementations.

Skills

Knowledge Graph

RDF

SPARQL

Python

Java

REST APIs

Graph Databases

SQL

Data Modeling

Data Pipelines

Education

Bachelor's degree or equivalent

Tools

GraphDB

Neo4j

Stardog

Milvus

Qdrant

Neptune

Job description

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%

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 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.

Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. We anticipate this job posting will be posted until 09/30/2026. 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. U.S. Employee Benefits | Accenture

Role Location Annual Salary Range

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

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