Data Engineer II - QuantumBlack, AI by McKinsey
Summary
Data Engineer II at McKinsey's QuantumBlack designing scalable, secure data pipelines and environments that power advanced analytics, ML, and AI projects for clients. Day-to-day involves building production-grade workflows with Python/PySpark, SQL, Airflow, Databricks, Docker, Kubernetes, and cloud platforms, plus contributing to R&D and internal AI assets.
As a Data Engineer II at McKinsey, you design scalable data pipelines and secure data environments to enable advanced analytics and ML. You will work with cross-functional teams and clients to turn data into insights, contributing to AI projects and R&D efforts. The role combines technical execution with collaboration across disciplines to deliver impactful data solutions at scale. You join a global, learning-driven practice that values ethics, diverse perspectives, and professional growth.
Pay / Benefits- competitive salary
- comprehensive benefits package
- world-class benefits
- global, diverse team
- continuous learning and apprenticeship culture
- Design scalable, modular data pipelines for machine learning and analytics
- Manage secure data environments and ensure data quality for analytics use cases
- Collaborate with clients and cross-functional teams to solve business problems with data
- Architect and build reproducible data processing workflows and ML pipelines
- Contribute to internal assets and R&D initiatives to broaden technical capabilities
- Assess data landscapes and prepare data for advanced analytics models
- Support adoption of AI solutions at scale in client environments
- Degree in Computer Science, Engineering, Mathematics, or equivalent experience
- 2–5+ years of professional data engineering experience
- Strong coding skills in Python, Scala, or Java
- Proven experience building production-grade data pipelines
- Experience with structured, semi-structured, and unstructured data
- Hands-on experience with Docker and Kubernetes
- Familiarity with Spark, Dask, cloud platforms (AWS, Azure, GCP) and analytics libraries (pandas, numpy, matplotlib)
- Exposure to DevOps/DataOps/MLOps concepts
- Experience with Python, PySpark, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, Docker, Kubernetes, and cloud services
- GenAI or agentic systems experience is a plus
- Client-facing or senior stakeholder management experience beneficial
- Excellent time management and communication; willingness to travel
- Strong communication skills (verbal and written)
- Cross-functional collaboration
- Adaptability and resilience
- Python
- PySpark
- SQL