Graph Data Engineer for GenAI & Knowledge Graphs

McKinsey & Company; Boston (MA); On-site; USD 138,000 - 142,000; Full time

Benefits offered by this job

Competitive salary

Comprehensive benefits package

Mentorship and growth opportunities

Job summary

McKinsey & Company Boston is seeking a hands-on data engineering professional to design and implement graph-centric data pipelines and a semantic data layer powering GenAI applications.

You will build a platform deployed across regions, focusing on AI capabilities, domain expertise, and human judgment, while developing entity models, taxonomies, and data ingestion workflows. Strong Python, AWS, and Docker skills are essential.

Qualifications

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

Responsibilities

• Build graph-centric data pipelines and a semantic data layer.
• Develop a production-grade Python codebase with tests.
• Operate AWS- and Docker-based infrastructure for scalable services.
• Design and evolve entity/relationship models and taxonomies.

Skills

Data engineering

Python development

AWS

Docker

Knowledge graphs

Relational databases

Generative AI

CI/CD

Problem solving

Team collaboration

Education

Degree in CS/Engineering or equivalent

Tools

Kedro

GitHub Actions

Job description

McKinsey & Company Boston is seeking a hands-on data engineering professional to design and implement graph-centric data pipelines and a semantic data layer powering GenAI applications.

You will build a platform deployed across regions, focusing on AI capabilities, domain expertise, and human judgment, while developing entity models, taxonomies, and data ingestion workflows. Strong Python, AWS, and Docker skills are essential.