GenAI Knowledge Graph Engineer

McKinsey & Company; New York (NY); On-site; USD 138,000 - 142,000; Full time

Benefits offered by this job

Comprehensive benefits

Job summary

McKinsey & Company is seeking a data engineering professional to build graph-centric data pipelines and a semantic data layer powering GenAI applications. You will design entity models, implement ingestion and enrichment, and publish data to graph stores.

You will write production-grade Python and manage CI/CD pipelines on AWS/Docker infrastructure. You will work across regions and functions, collaborating with cross-functional teams to deliver scalable knowledge graph services and drive

Qualifications

• Degree in CS/Engineering or equivalent experience.
• 3+ years of relevant experience with data engineering.
• Experience with cloud-based data engineering using AWS services.
• Expertise with Python development and relational databases.
• Experience with knowledge graphs and data pipelines.
• Strong testing, modularity, and CI/CD practices.
• Hands-on Docker experience and scalable data services.

Responsibilities

• Build graph-centric data pipelines and a semantic data layer to power GenAI applications.
• Design and evolve entity/relationship models and taxonomies.
• Implement ingestion, normalization, and enrichment mapping sources to graph schemas.
• Perform entity resolution and semantic tagging; validate structures.
• Publish data to graph stores and maintain production-grade code and CI/CD pipelines.

Skills

Python development

AWS cloud

Data pipelines

Knowledge graphs

Docker

Testing automation

Relational databases

Generative AI interest

Kedro framework

Cross-functional collaboration

Education

Degree in CS/Engineering

Tools

Docker

AWS

Kedro

Job description

McKinsey & Company is seeking a data engineering professional to build graph-centric data pipelines and a semantic data layer powering GenAI applications. You will design entity models, implement ingestion and enrichment, and publish data to graph stores.

You will write production-grade Python and manage CI/CD pipelines on AWS/Docker infrastructure. You will work across regions and functions, collaborating with cross-functional teams to deliver scalable knowledge graph services and drive