Knowledge Graph Engineer

We are looking for an experienced Knowledge Graph Engineer to design, build, and scale a production-grade property graph platform that powers customer segmentation, device intelligence, and household-level insights. You will own the full lifecycle from schema design and bulk ingestion to real-time CDC pipelines and graph embedding working closely with the segment engine team to deliver high-performance traversal queries and ML-ready embeddings at scale.

Key Responsibilities:

• Schema Design: Architect the property graph schema defining node types Customer, Device, Account, Plan, Offer and edge types HAS_DEVICE, ON_PLAN, SHARES_HOUSEHOLD, SIMILAR_TO ensuring optimal cardinality and partition key design for scale.
• Bulk Load Pipeline: Build and validate the initial bulk load job across the ingestion stack (e.g., Delta Lake ? S3 staging ? Neptune bulk loader or equivalent technology).
• Real-Time CDC Pipeline: Implement a change data capture pipeline (e.g., Cosmos DB Change Feed ? Kafka ? Flink ? Neptune writer) with an end-to-end lag target of <60 seconds.
• Query Development: Write and optimize Gremlin traversal queries for household segmentation, device-sharing patterns, and account-linked segmentation use cases.
• Index Strategy: Design vertex-centric indexes and leverage Neptune Analytics HNSW for embedding-based similarity lookups.
• Graph Embeddings: Build a Node2Vec embedding pipeline (SageMaker or Databricks) and load SIMILAR_TO edges to support ML-driven similarity features.
• Documentation: Document schema definitions, traversal patterns, and query performance benchmarks for consumption by the segment engine team.

Must-Have Skills & Qualifications:

• 4+ years of graph database engineering experience with production Gremlin / TinkerPop expertise.
• AWS Neptune or equivalent cloud graph database bulk loader operations, instance sizing, HA configuration, and VPC networking.
• Apache Kafka and Apache Flink for CDC pipeline design and implementation.
• Property graph data modelling entity resolution, edge cardinality, and partition key design.
• Graph traversal performance profiling at scale (100M+ nodes).

Nice-to-Have Skills:

• Graph embedding algorithms Node2Vec, GraphSAGE, or similar.
• Neptune Analytics experience for graph analytics workloads.
• Neo4j migration or comparative architecture experience (trade-offs vs. Neptune at scale).
• Python (gremlinpython) and Java traversal source authoring.
• AWS SageMaker or Azure Databricks for embedding model training.

What We Offer:

• Opportunity to architect and own a greenfield knowledge graph platform at enterprise scale.
• Work with cutting-edge graph and ML technologies across AWS, Kafka, Flink, and SageMaker ecosystems.
• Collaborate with data engineering, ML, and product teams to drive real customer and business impact.
• Competitive compensation, flexible work arrangements, and a culture of continuous learning.

TekWissen Group is an equal opportunity employer supporting workforce diversity.