Knowledge Graph Architect & AI Data Engineer

Accenture PLC; San Diego (CA); On-site; USD 94,000 - 294,000; Full time

Job summary

Accenture is seeking a hands-on Big Data/AI specialist to architect production knowledge graph solutions, translate semantic models into scalable implementations, and build ingestion pipelines. You will integrate graph databases with data lakes and deploy high-throughput services for AI-enabled analytics.

You’ll work across GraphRAG, Databricks/Spark, and modern data platforms while mentoring engineers and ensuring low latency, high availability, and production readiness.

Qualifications

• Minimum of 5 years of hands-on experience in Big Data engineering, data engineering, or analytics engineering, including development of scalable ETL/ELT pipelines for batch and/or streaming workloads.
• Minimum 2 years in Python, with hands-on experience using relevant libraries and frameworks such as PySpark, RDFLib, NetworkX, and FastAPI.
• Minimum 2 years experience with graph query and data languages such as SPARQL, Cypher, RDF, and/or SQL.
• 2 years knowledge graph and graph database technologies, including experience with platforms such as Neo4j, Stardog, GraphDB, and/or Amazon Neptune.

Responsibilities

• Architect, develop, and implement production knowledge graph solutions using RDF triple stores and/or property graph technologies.
• Translate semantic architectures, ontologies, and enterprise data models into scalable technical implementations.
• Build automated data ingestion and transformation pipelines using Python and PySpark to map relational, document, streaming, and unstructured data into graph representations.
• Design and implement identity resolution, entity disambiguation, entity linking, and link prediction capabilities to create unified, canonical graph records at enterprise scale.
• Integrate graph databases with enterprise data lakes, lakehouses, analytics platforms, APIs, and downstream applications.
• Develop high-throughput REST, GraphQL, and/or gRPC services that expose graph traversal, semantic search, and graph query capabilities.
• Design and implement GraphRAG and hybrid vector-graph solutions that ground Large Language Models using contextual enterprise knowledge.
• Develop integration layers that connect knowledge graphs, vector stores, enterprise data platforms, and Generative AI applications.
• Build data processing and retrieval capabilities that enable LLMs and AI agents to leverage trusted, contextualized enterprise information.
• Partner with AI, data, ontology, and application engineering teams to operationalize knowledge-driven AI solutions.
• Leverage Databricks, Apache Spark, or comparable technologies to perform distributed graph transformations, large-scale entity resolution, and semantic data processing.
• Develop production-grade ETL/ELT pipelines supporting batch, streaming, structured, semi-structured, and unstructured data.
• Create automated testing, schema validation, and data-quality frameworks using technologies and approaches such as SHACL and Cypher constraints.
• Profile and optimize SPARQL, Cypher, and/or Gremlin query execution for high-volume and high-concurrency environments.
• Design indexing, caching, partitioning, and query optimization strategies to improve graph performance and scalability.
• Establish CI/CD practices for ontology and graph deployments, version control, automated testing, and infrastructure-as-code across cloud environments.
• Design solutions for high availability, low latency, scalability, observability, and production resiliency.
• Mentor senior and mid-level engineers in knowledge graph engineering, Python development, distributed data processing, and performance optimization.

Skills

Python

PySpark

Graph database

RDF graphs

Big Data engineering

Generative AI

GraphRAG

Tools

Neo4j

Stardog

GraphDB

Amazon Neptune

Databricks

Apache Spark

SHACL

Cypher

SPARQL

Gremlin

Job description

Accenture is seeking a hands-on Big Data/AI specialist to architect production knowledge graph solutions, translate semantic models into scalable implementations, and build ingestion pipelines. You will integrate graph databases with data lakes and deploy high-throughput services for AI-enabled analytics.

You’ll work across GraphRAG, Databricks/Spark, and modern data platforms while mentoring engineers and ensuring low latency, high availability, and production readiness.