Senior Knowledge Graph Data Engineer for GenAI Platform

McKinsey & Company; New York (NY); On-site; USD 176,000 - 179,500; Full time

Benefits offered by this job

Medical coverage

Mental health coverage

Dental and vision coverage

Telemedicine services

Life insurance

Parental leave and family planning

Retirement contributions

Paid time off

Job summary

McKinsey & Company in New York is seeking a data engineering professional to build graph-centric data pipelines and a semantic data layer powering GenAI applications. You will design scalable platforms deployed across regions and functions, blending AI capabilities with domain expertise and human judgment.

Responsibilities include designing entity/relationship models, implementing ingestion and enrichment, performing entity resolution, and publishing data to graph stores.

Qualifications

• Degree in CS/Engineering or equivalent experience.
• 5+ years of relevant experience in data engineering and cloud-based workloads.
• Experience with Python development and relational databases.
• Experience with knowledge graphs and Docker.
• Strong coding standards, testing and modular design.
• Excellent collaboration in cross-functional teams.

Responsibilities

• Build graph-centric data pipelines and a semantic data layer for GenAI applications.
• Design and evolve entity/relationship models and taxonomies.
• Implement ingestion, normalization, and enrichment mapping sources to graph schemas.
• Write production-grade Python, maintain CI/CD pipelines, and operate AWS- and Docker-based infrastructure.

Skills

Python development

Docker

AWS cloud

Knowledge graphs

Data engineering

Relational databases

Kedro framework

Problem solving

Cross-functional teamwork

Generative AI interest

Education

Degree in CS/Engineering

Tools

GitHub Actions

CI/CD

SQL

Pytest

Job description

McKinsey & Company in New York is seeking a data engineering professional to build graph-centric data pipelines and a semantic data layer powering GenAI applications. You will design scalable platforms deployed across regions and functions, blending AI capabilities with domain expertise and human judgment.

Responsibilities include designing entity/relationship models, implementing ingestion and enrichment, performing entity resolution, and publishing data to graph stores.