AI Data Engineer: Build Scalable AI Data Pipelines

McKinsey & Company; San Francisco (CA); On-site; USD 122,000 - 125,000; Full time

Job summary

McKinsey & Company is seeking a data engineer in the United States to build foundational data infrastructure for AI applications. You will design scalable data pipelines, manage secure data environments, and prepare data for AI systems while partnering with cross-functional teams across industries.

You will translate hypotheses into engineered features, contribute to R&D for next-generation AI capabilities, and develop scalable data components for ML and agentic AI systems.

Qualifications

• Expected graduation between December 2026 and August 2027 in an engineering degree.
• 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 within AI, Generative AI, ML, or BI across data formats and processing methods.
• Familiarity with data platforms and cloud services (AWS, Azure, GCP) and tools (Pandas, Spark, dbt, LangChain).
• Exposure to modern software development practices, including Git, DevOps, MLOps/LLMOps, and CI/CD concepts.
• Excellent time management in autonomous work environments.
• Willingness to learn quickly and adapt to different projects and tech stacks.
• Experience using coding agents (Cursor, Claude Code, Codex) is a plus.
• Ability to travel up to 80% and maintain regular in-office presence.
• Strong communication skills in English and local office language(s).

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, and prepare data for AI-driven systems with cross-functional teams and clients.
• Translate hypotheses into engineered features and contribute to R&D initiatives focused on scaling next-generation AI capabilities.
• Develop scalable data components for ML, agentic, and autonomous AI systems while assessing data landscapes and ensuring data quality.
• Collaborate with QuantumBlack, AI by McKinsey, and QuantumBlack Labs to develop AI capabilities and enterprise solutions.
• Work in cross-functional Agile teams with Data Scientists and ML Engineers; travel and client-facing work as needed.

Skills

Python

SQL

Data engineering

Git

DevOps basics

LLMOps basics

Education

Bachelor's or Master's in Engineering

Tools

Databricks

Snowflake

BigQuery

PostgreSQL

Pandas

Spark

dbt

LangChain

Job description

McKinsey & Company is seeking a data engineer in the United States to build foundational data infrastructure for AI applications. You will design scalable data pipelines, manage secure data environments, and prepare data for AI systems while partnering with cross-functional teams across industries.

You will translate hypotheses into engineered features, contribute to R&D for next-generation AI capabilities, and develop scalable data components for ML and agentic AI systems.