Data Engineer for AI & Agentic Systems

McKinsey; Greater London; Hybrid; GBP 70,000 - 120,000; Full time

Job summary

McKinsey in London is seeking a data engineer to build foundational data infrastructure powering AI applications and agentic architectures. You will design scalable data pipelines, manage secure environments, and collaborate with cross-functional teams and clients to deliver production-ready AI systems.

You will grow through mentoring, apprenticeship, and exposure to diverse projects across industries, working within Agile teams and with Data Scientists, ML Engineers, and industry experts to

Qualifications

• Degree in Computer Science/Engineering, or equivalent experience.
• 1+ years of relevant professional experience building and deploying data solutions.
• Strong proficiency in Python and SQL for data engineering and experience writing robust, production-grade code, including deploying code across environments.
• Proven experience building end-to-end data pipelines and platforms for Agentic AI, Generative AI, Machine Learning, or Business Intelligence, covering data preparation, embeddings generation, vector search, and system integration using modern frameworks (Spark, dbt, LangChain).
• A strong foundation in system design, data storage, and reliability with commonly used data platforms (Databricks, Snowflake, BigQuery, PSQL, etc.) and data engineering tools (e.g., Pandas, Spark, dbt, etc.).
• Hands-on experience with MLOps/LLMOps principles, including CI/CD for data workflows, automated agent evaluation (LangSmith, Opik, Langfuse), and infrastructure as code (Terraform).
• Experience building systems with different data formats (structured vs unstructured) and data processing methods (streaming vs batch) and deploying across major cloud platforms (AWS, Azure, GCP).
• Exceptional time management in a complex and largely autonomous work environment.
• Commercial client-facing or senior stakeholder management experience is beneficial.
• Experience using coding agents (Cursor, Claude Code, Codex, etc.) is a plus.
• Strong communication skills, both verbal and written, in English and local office language(s).

Responsibilities

• Build the foundational data infrastructure that powers AI applications leveraging LLMs, retrieval systems, workflows, and agentic architectures.
• Design and maintain scalable data pipelines, manage secure data environments, and prepare data for AI-driven systems.
• Collaborate with cross-functional teams and clients across industries to develop scalable AI capabilities and enterprise data solutions.
• Translate hypotheses into engineered features and contribute to R&D initiatives focused on scaling AI capabilities.
• Support McKinsey teams to accelerate AI adoption and deliver meaningful client impact.
• Work with QuantumBlack, AI by McKinsey, and QuantumBlack Labs on innovative data engineering projects.

Skills

Python

SQL

Data pipelines

Agentic AI

Spark

dbt

LangChain

MLOps

Cloud platforms

Communication

English

Stakeholder management

Education

Bachelor's degree in Computer Science/Engineering or equivalent

Tools

Databricks

Snowflake

BigQuery

Pandas

Spark

dbt

LangChain

Terraform

LangSmith

Langfuse

Job description

McKinsey in London is seeking a data engineer to build foundational data infrastructure powering AI applications and agentic architectures. You will design scalable data pipelines, manage secure environments, and collaborate with cross-functional teams and clients to deliver production-ready AI systems.

You will grow through mentoring, apprenticeship, and exposure to diverse projects across industries, working within Agile teams and with Data Scientists, ML Engineers, and industry experts to