Data Engineer I - QuantumBlack, AI by McKinsey

Overview

As a Data Engineer I at McKinsey, you design and maintain scalable data pipelines and secure data environments to enable advanced analytics and ML. You collaborate with cross-functional teams and clients to address real-world data challenges and contribute to AI solutions. The role sits within a global Data Engineering community in London, offering mentorship, continuous learning, and opportunity to grow as a technologist and leader. You impact business value by delivering reliable data foundations at scale.

Pay / Benefits

• competitive salary
• comprehensive benefits package
• world-class learning and apprenticeship culture

Responsibilities

• Design and build scalable data pipelines for machine learning and analytics
• Maintain secure data environments and ensure data governance
• Prepare data for advanced analytics models and insights
• Collaborate with Data Scientists, ML Engineers, and clients across cross-functional teams
• Contribute to R&D projects and internal asset development
• Assess data landscapes and ensure data quality across environments

Key requirements

• Degree in Computer Science, Engineering, Mathematics, or equivalent experience
• Up to 2 years’ experience building data pipelines (internships ok)
• Proficiency in Python and clean, scalable code
• Familiarity with pandas, numpy, matplotlib; Spark or Dask; cloud platforms (AWS, Azure, GCP)
• Basic exposure to Docker and Kubernetes; understanding of DevOps, DataOps, MLOps advantageous
• Experience with PySpark, PyData stack, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS; Docker; Kubernetes; cloud providers
• Experience with Generative AI (GenAI) and agentic systems considered a strong plus
• Excellent time management and organizational skills
• Strong written and verbal English communication; ability to adapt to audiences
• Willingness to travel

• time management
• communication
• adaptability
• Python
• PySpark
• Pandas