You Are:
You are the technical authority responsible for turning semantic architectures and ontology models into robust,high-performance,and production-ready knowledge systems.You bridge the gap between semantic modeling,software engineering,and modern Big Data analytics infrastructure,building scalable graph data pipelines,microservices,and GraphRAG integration layers.
You stay hands-on in code:you write production ingestion jobs primarily inPython,tune complex graph queries,integrate graph databases (RDF Triple Stores / Property Graphs) with modern enterprise data lakes,and ensure high availability,low latency,and deterministic reasoning for downstream analytics and AI applications.
The Work:
- 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.
Generative AI & GraphRAG
- 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.
Big Data & Production Engineering
- 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.
Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.
Here9s What You Need- 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.
Bonus Points If You Have
- Hands-on experience with Databricks or comparable modern data platforms, including PySpark, Spark SQL, Delta Lake, Snowflake, Unity Catalog, and/or enterprise lakehouse architectures.
- Experience developing lakehouse-to-graph ingestion pipelines and large-scale semantic data transformation workloads.
- Experience implementing GraphRAG, Retrieval-Augmented Generation (RAG), vector search, and hybrid vector-graph architectures for Generative AI applications.
- Experience with SHACL, graph schema validation, ontology versioning, and semantic data-quality frameworks.
- Experience implementing RDF and/or property graph architectures in production environments.
- Experience with regulatory or compliance-driven modeling.
- Graph-based ML alongside symbolic modeling.
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. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 12/04/2026.
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:
Role Location Annual Salary Range
California $94,400 to $293,800
Colorado $94,400 to $253,800
Connecticut $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
Ohio $87,400 to $235,000
Virginia $87,400 to $270,300
Washington $100,500 to $270,300
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