AI Data Engineer Intern: Build Scalable Data Pipelines

McKinsey & Company; Raleigh (NC); On-site; USD 122,000 - 125,000; Full time

Benefits offered by this job

Medical coverage

Mental health support

Retirement contributions

Job summary

McKinsey & Company is building foundational data infrastructure to power next-generation AI applications across industries. You will design scalable data pipelines, manage secure data environments, and prepare data for AI/ML systems.

You’ll collaborate with cross-functional teams and clients, translating hypotheses into engineered features while ensuring data quality and reproducibility. Expect to work with Databricks, Snowflake, and other platforms as part of a global Data Engineering community.

Qualifications

• Upcoming graduation between December 2026-August 2027 with a degree in an engineering discipline.
• Experience in a data-focused role (internships, academic projects acceptable).
• Ability to write clean, well-documented code in Python and SQL.
• Interest in data engineering in Agentic AI, Generative AI, ML, or BI across data formats.

Responsibilities

• Build foundational data infrastructure powering AI applications, using LLMs, retrieval systems, workflows and agentic architectures.
• Design and maintain scalable data pipelines; manage secure data environments; prepare data for AI systems.
• Collaborate with cross-functional teams and clients to develop enterprise AI capabilities.
• Translate hypotheses into engineered features; apply data quality fundamentals; prepare data for AI solutions.
• Work with Data Scientists, ML Engineers, and industry experts to deliver AI solutions.

Skills

Data-focused experience

Python

SQL

Communication

Travel up to 80%

Git

MLOps

Cloud platforms (AWS/GCP/Azure)

Data engineering concepts

Education

Bachelor’s or Master’s in Engineering

Tools

Pandas

Spark

dbt

LangChain

Cursor

Claude Code

Codex

Job description

McKinsey & Company is building foundational data infrastructure to power next-generation AI applications across industries. You will design scalable data pipelines, manage secure data environments, and prepare data for AI/ML systems.

You’ll collaborate with cross-functional teams and clients, translating hypotheses into engineered features while ensuring data quality and reproducibility. Expect to work with Databricks, Snowflake, and other platforms as part of a global Data Engineering community.