AI Data Engineer: Shape Scalable AI Data Pipelines

McKinsey & Company; New York (NY); On-site; USD 122,000 - 125,000; Full time

Benefits offered by this job

World-class benefits

Global community

Job summary

McKinsey & Company is seeking a data-engineering oriented candidate to help build foundational data infrastructure powering AI applications. You will design scalable pipelines, manage secure data environments, and prepare data for AI-driven systems while collaborating with cross-functional teams and clients.

You will contribute to the development of next-generation AI capabilities and grow as a technologist across industries, within a global Data Engineering community.

Qualifications

• Upcoming graduation between Dec 2026–Aug 2027 with an engineering degree.
• Experience in a data-focused role (internships/projects acceptable).
• Ability to write clean code in Python and SQL.
• Interest in data engineering for AI, ML, or BI.
• Familiarity with data platforms and cloud services.
• Experience with version control and CI/CD concepts.

Responsibilities

• Build foundational data infrastructure for AI applications using LLMs and pipelines.
• Design and maintain scalable data pipelines and secure data environments.
• Collaborate with cross-functional teams and clients to prepare data for AI systems.
• Translate hypotheses into engineered features and contribute to R&D for AI capabilities.

Skills

Data-focused experience

Python

SQL

Data engineering

Agentic AI

ML/LLMOps

CI/CD

Git

Travel up to 80%

Education

Bachelor's or Master's in engineering

Tools

Databricks

Snowflake

BigQuery

Pandas

Spark

dbt

LangChain

Job description

McKinsey & Company is seeking a data-engineering oriented candidate to help build foundational data infrastructure powering AI applications. You will design scalable pipelines, manage secure data environments, and prepare data for AI-driven systems while collaborating with cross-functional teams and clients.

You will contribute to the development of next-generation AI capabilities and grow as a technologist across industries, within a global Data Engineering community.