Job Description

As a Senior Knowledge Graph Data Engineer, you will build graph-centric data pipelines and a semantic data layer that powers Generative AI applications. You will work with a diverse team to design entity/relationship models and implement data ingestion and normalization processes, all while writing production-grade Python code and managing cloud-based infrastructure.

Requirements

Degree in CS/Engineering or equivalent experience
7+ years of professional experience as a data engineer, with a strong focus on cloud-based data engineering using AWS services
Expertise with Python development
Experience with knowledge graphs
Practicing high coding standards with clean code, modularity, error handling, testing automation and more
Strong experience with relational databases
Very driven, super strong on execution and output orientation
Hands-on experience with Docker
Solid and demonstrable background in data pipeline performance and diagnostics
Interest in Generative AI and other ML topics
Excellent problem-solving skills and the ability to analyze and resolve complex data engineering challenges
Strong communication and collaboration skills

Responsibilities

Build graph-centric data pipelines and a semantic data layer for GenAI applications
Design and evolve entity/relationship models, taxonomies, and identifiers
Implement ingestion, normalization, and enrichment that maps heterogeneous sources to graph schemas
Perform entity resolution and semantic tagging
Validate structures and publish data to graph stores
Write production-grade Python code with comprehensive tests
Create and modify GitHub Actions CI/CD pipelines
Operate AWS- and Docker-based infrastructure to deliver reliable, scalable knowledge graph data services

Benefits

Employees at McKinsey are often offered a comprehensive benefits package, including medical, dental, and vision coverage, prescription benefits, mental-health resources, expert medical opinions, and travel emergency protection. Financial benefits typically include retirement contributions and savings plans, life/accident/disability insurance, tax-advantaged accounts, and financial coaching. Many roles also feature generous paid time off (including volunteering days), flexible/hybrid work options, family care support (parental leave, fertility/adoption/surrogacy assistance, backup care), tuition reimbursement, transportation benefits, discounts, and matching gifts.