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Data Engineer II - QuantumBlack, AI by McKinsey

Skills

agentic aiagileaiawsazurebigqueryci cdclaude codeclouddata engineeringdata pipelinesdata qualitydatabricksdbtembeddingsfeature engineeringgcplangchainllmopsmachine learningmlopspandaspostgresqlpython

Description

Overview

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
Responsibilities
  • 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
Key requirements
  • 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

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