Data Engineer II - QuantumBlack, AI by McKinsey
Summary
Designs and maintains scalable data pipelines and secure data foundations that power AI/ML applications and agentic systems at QuantumBlack, McKinsey's AI arm, working in Agile teams with data scientists, ML engineers, and clients. Core stack: Python, SQL, Spark, dbt, Databricks/Snowflake/BigQuery, and AWS/Azure/GCP with MLOps/LLMOps practices.
As a Data Engineer II at McKinsey, you will design and maintain scalable data pipelines that power cutting-edge AI applications and agentic architectures. You will collaborate with cross‑functional teams and clients to translate data into high‑impact AI solutions, shaping next‑generation systems at scale. This role sits within a global data engineering community, offering mentorship and a clear path to growth through structured learning and hands‑on projects. You’ll work in London, contributing to measurable business value while advancing your data and AI expertise.
Pay / Benefits- competitive salary
- comprehensive benefits package
- mentorship and structured learning programs
- global collaboration across 65+ countries
- exposure to diverse AI initiatives
- career development opportunities
- Build and maintain scalable data pipelines and data foundations for AI systems
- Design data architectures and secure data environments for production use
- Prepare data for AI/ML workflows, embeddings, and vector search
- Collaborate with Data Scientists, ML Engineers, and clients in cross-functional Agile teams
- Contribute to R&D initiatives to scale next‑generation AI capabilities
- Support data quality, landscape assessment, and feature engineering
- Collaborate with QuantumBlack and Labs teams to develop enterprise AI solutions
- Assist in deploying production-grade data solutions across cloud platforms
- 2-5+ years of relevant experience in data engineering or similar
- Strong Python and SQL for production-grade code
- Experience building end-to-end data pipelines for AI/ML/BI
- Proficiency with data platforms (Databricks, Snowflake, BigQuery, PostgreSQL) and tools (Pandas, Spark, dbt)
- Hands-on MLOps/LLMOps knowledge including CI/CD for data workflows
- Cloud experience across AWS, Azure, GCP
- Strong communication in English and local language(s)
- Client-facing or senior stakeholder management experience is beneficial
- Experience with coding agents (Cursor, Claude Code, Codex) is a plus
- Familiarity with LangChain, embeddings, vector search is a plus
- Strong communication and collaboration
- Client-facing demeanor
- Resilience and adaptability
- Python
- SQL
- Spark