Knowledge Graph Engineer

We are seeking an experienced Knowledge Graph Engineer with strong expertise in Graph Databases, AWS Neptune, Neo4j, TigerGraph, Gremlin, Cypher, Property Graph Modeling, Kafka, Flink, and Graph Embeddings. The ideal candidate will design, build, and scale a production-grade property graph platform supporting customer segmentation, device intelligence, and household-level insights.Candidate RequirementsExperience: 4+ years of production Graph Database EngineeringPrimary Skill Set: Knowledge Graph Engineer with Graph Database and Graph Traversal expertiseGraph Database: AWS Neptune OR Neo4j OR TigerGraph – MandatoryGraph Query Language: Gremlin OR Cypher – MandatoryStrong experience with large-scale graph data platforms and property graph modeling.Required SkillsGraph Database EngineeringAWS Neptune / Neo4j / TigerGraphGremlin / CypherProperty Graph Data ModelingGraph Schema DesignGraph Traversal OptimizationApache KafkaApache FlinkCDC PipelinesBulk Data LoadingGraph IndexingPartition Key DesignEntity ResolutionLarge-Scale Graph ProcessingRequired Qualifications4+ years of hands-on experience with Graph Database Engineering in production environments.Strong experience with Gremlin / TinkerPop or Cypher.Experience designing and implementing Property Graph Data Models.Experience with AWS Neptune or equivalent cloud graph database platforms.Experience with graph bulk loading, ingestion, instance sizing, HA, and networking.Strong experience designing Kafka and Flink-based CDC pipelines.Experience with Graph Traversal Performance Optimization at scale.Experience working with large graph datasets, including 100M+ nodes.Strong understanding of entity relationships, edge cardinality, indexing, and partitioning strategies.Preferred QualificationsExperience with AWS Neptune Analytics.Experience with Neptune Analytics HNSW and similarity search.Experience with Graph Embeddings.Experience with Node2Vec, GraphSAGE, or similar algorithms.Experience with Python / Gremlin-Python.Experience with Java / TinkerPop.Experience with AWS SageMaker or Databricks for embedding model training.Experience with Neo4j migration or Neptune architecture comparisons.Experience with ML-driven graph similarity and SIMILAR_TO relationships.Key ResponsibilitiesDesign scalable Property Graph Schemas for Customer, Device, Account, Plan, and Offer entities.Define graph relationships including HAS_DEVICE, ON_PLAN, SHARES_HOUSEHOLD, and SIMILAR_TO.Build and validate Bulk Load Pipelines from data lake/staging platforms into AWS Neptune or equivalent graph databases.Implement real-time CDC Pipelines using Kafka and Flink.Develop and optimize Gremlin Traversal Queries for customer segmentation, household insights, and device-sharing patterns.Design Graph Indexing, Partitioning, and Cardinality Strategies for enterprise-scale workloads.Build Graph Embedding Pipelines using Node2Vec / GraphSAGE.Load embedding-based SIMILAR_TO relationships to support ML-driven similarity features.Profile and optimize graph traversal performance across 100M+ nodes.Document graph schemas, traversal patterns, architecture, and query performance benchmarks.Collaborate with Data Engineering, ML, and Product teams to deliver scalable graph-based solutions.ABOUT BRICKRED SYSTEMSBrickRed Systems is a global leader in next-generation technology consulting and workforce solutions, specializing in delivering high-quality talent across digital, engineering, marketing, analytics, finance, operations, and business transformation domains. With a strong emphasis on innovation, scalability, and client success, BrickRed Systems helps organizations solve complex business challenges by providing skilled professionals across strategy, technology, creative, and operational functions. BrickRed fosters a culture of continuous learning, collaboration, and excellence, enabling professionals to contribute to high-impact global initiatives while advancing their careers.