Role: Senior Graph Data Engineer (Neo4j & AI Knowledge Graphs)
Experience: 8+ years
Type: Contract
We’re hiring a
Graph Data Engineer
to design and implement advanced
Neo4j-powered knowledge graph systems
for our next-gen AI platform. You'll work at the intersection of
data engineering, AI/ML, and financial services
, helping build the graph infrastructure that powers semantic search, investment intelligence, and automated compliance for venture capital and private equity clients.This role is ideal for engineers who are passionate about
graph data modeling
,
Neo4j performance
, and enabling
AI-enhanced analytics
through structured relationships.What You'll Do
- Design Knowledge Graphs: Build and maintain Neo4j graph schemas modeling complex fund administration relationships — investors, funds, companies, transactions, legal docs, etc.
- Graph-AI Integration: Work with GenAI teams to power RAG systems, semantic search, and graph-enhanced NLP pipelines.
- ETL & Data Pipelines: Develop scalable ingestion pipelines from sources like FundPanel.io, legal documents, and external market feeds using Python, Spark, or Kafka.
- Optimize Graph Performance: Craft high-performance Cypher queries, leverage APOC procedures, and tune for real-time analytics.
- Graph Algorithms & Analytics: Implement algorithms for fraud detection, relationship scoring, compliance, and investment pattern analysis.
- Secure & Scalable Deployment: Implement clustering, backups, and role-based access on Neo4j Aura or containerized environments.
- Collaborate Deeply: Partner with AI/ML, DevOps, data architects, and business stakeholders to translate use cases into scalable graph solutions.
What You Bring
- 7+ years in software/data engineering; 2+ years in Neo4j and Cypher.
- Strong experience in graph modeling, knowledge graphs, and ontologies.
- Proficiency in Python, Java, or Scala for graph integrations.
- Experience with graph algorithms (PageRank, community detection, etc.).
- Hands-on with ETL pipelines, Kafka/Spark, and real-time data ingestion.
- Cloud-native experience (Neo4j Aura, Azure, Docker/K8s).
- Familiarity with fund structures, LP/GP models, or financial/legal data a plus.
- Strong understanding of AI/ML pipelines, especially graph-RAG and embeddings.
Use Cases You'll Help Build
- AI Semantic Search over fund documents and investment entities.
- Investment Network Analysis for GPs, LPs, and portfolio companies.
- Compliance Graphs modeling fund terms and regulatory checks.
- Document Graphs linking LPAs, contracts, and agreements.
- Predictive Investment Models enhanced by graph relationships.
Skills: java,machine learning,spark,apache spark,neo4j aura,ai,azure,cloud-native technologies,data,ai/ml pipelines,scala,python,cypher,graphs,ai knowledge graphs,graph data modeling,apoc procedures,semantic search,etl pipelines,data engineering,neo4j,etl,cypher query,pipelines,graph schema,kafka,kafka streams,graph algorithms
