Data Engineer: Scalable AI Data Pipelines

McKinsey; Lombardia; Hybrid; EUR 41,000 - 50,000; Full time

Job summary

McKinsey in Milan is seeking a data engineer to build foundational data infrastructure for AI applications and to design scalable data pipelines. You will collaborate with Data Scientists, ML Engineers, and clients across industries to prepare data for AI-driven systems and enable scalable, production-ready AI capabilities.

The role emphasizes translating hypotheses into engineered features, managing secure data environments, and contributing to R&D initiatives in agentic AI.

Qualifications

• Degree in Computer Science/Engineering or equivalent experience.
• Experience in a data-focused role (internships, academic projects).
• Ability to write clean, well-documented code in Python/SQL.
• Interest in data engineering for Agentic AI, Generative AI, ML, BI.
• Familiarity with Databricks/Snowflake/BigQuery and cloud platforms.
• Experience with Git and CI/CD, MLOps basics.
• Strong time management in autonomous environment.
• Willingness to learn and adapt to different stacks.
• Experience with coding agents is a plus.
• Strong English communication.

Responsibilities

• Build foundational data infrastructure powering AI applications.
• Design and maintain scalable data pipelines and secure data environments.
• Collaborate with data scientists, ML engineers, and clients across industries.
• Prepare data for AI-driven systems and translate hypotheses into features.

Skills

Python

SQL

Git

Time management

English communication

Education

Degree in Computer Science/Engineering

Tools

Pandas

Spark

dbt

LangChain

Databricks

Snowflake

BigQuery

AWS

Azure

GCP

PSQL

Job description

McKinsey in Milan is seeking a data engineer to build foundational data infrastructure for AI applications and to design scalable data pipelines. You will collaborate with Data Scientists, ML Engineers, and clients across industries to prepare data for AI-driven systems and enable scalable, production-ready AI capabilities.

The role emphasizes translating hypotheses into engineered features, managing secure data environments, and contributing to R&D initiatives in agentic AI.