Knowledge Graph Engineer

Responsibilities

• Implement graph schemas including entity types, relationship types, property definitions, and provenance attributes against client-provided specifications.
• Design and implement concept linking logic connecting entities across graph boundaries with full provenance tracking.
• Manage schema versioning and schema evolution without requiring full re-ingestion.
• Build idempotent batch ETL pipelines ingesting structured data from FHIR APIs, ontology datasets, and metadata sources into a graph database at scale.
• Write and optimise graph queries (Cypher, SPARQL, or Gremlin) for production access patterns: entity lookup, multi-hop traversal, hierarchy navigation, and lineage queries.
• Tune query performance to meet latency and concurrency targets as the graph scales to tens of millions of records.
• Expose graph access patterns as MCP tools for consumption by LLM-based orchestration agents.
• Define and execute retrieval quality benchmarks: precision, recall, traversal accuracy, latency against test query sets and produce validation reports.
• Author operational runbooks, schema versioning procedures, and handoff documentation
• MUST-HAVE REQUIREMENTS
• Minimum 5 years overall experience with at least 3 years in production knowledge graph or graph database engineering.
• Knowledge Graph Engineering:
• Production-grade experience building knowledge graphs: entity modelling,
• relationship typing, provenance attributes, and schema versioning in a live
• operational system with concurrent users and continuous data updates.
• Graph Database Platforms
• Hands-on experience deploying and operating at least one graph DB
• platform in production: Neo4j, Amazon Neptune, TigerGraph, Google
• Spanner Graph, Cosmos DB (Gremlin API), Stardog, or equivalent. Able to
• configure, tune, and troubleshoot under load.
• Graph Query Languages
• Proficient in at least one of Cypher, SPARQL, or Gremlin. Able to write and
• optimise queries for production latency targets and design access patterns
• for multi-hop traversals at scale.
• Ingestion Pipelines
• Able to build production batch and streaming data pipelines end-to-end. Experience with at least one streaming framework (Kafka, Pub/Sub,
• Kinesis) and one processing or orchestration tool (Airflow, Apache Beam /
• Dataflow, Spark).
• API Development
• Experience designing and building versioned REST APIs for graph data
• exposure with typed schemas, access control, audit logging, and API
• gateway integration.
• Cloud Platforms
• Hands-on with GCP or Azure: IAM, secrets management, managed service
• deployment, networking, and Terraform for infrastructure-as-code.
• Regulated Data Environments
• Experience with sensitive or regulated data (PHI, PII, or equivalent) where
• access control, audit logging, and compliance reviews are standard
• practice. Healthcare, life sciences, pharma, or financial services
• backgrounds are all relevant.
• Programming
• Production-grade Python for pipeline development and data transformation.
• Maintainable, testable code with proper error handling.
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us !

Benefits

Additional Information

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth. If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi! Role: Knowledge Graph Engineer Experience Level: 5 to 7 Years Work location: Mumbai/Bangalore/Trivandrum

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