Data Engineer II: Scalable AI Data Pipelines

McKinsey; New York (NY); On-site; USD 147,000 - 150,000; Full time

Job summary

McKinsey & Company seeks a data engineering professional to build foundational data infrastructure powering AI applications, including LLMs, retrieval systems, and agentic architectures. You will design scalable pipelines, secure data environments, and prepare data for AI-driven systems while collaborating with cross-functional teams and clients.

You will work with Data Scientists, ML Engineers, and industry experts to deliver end-to-end AI solutions, translate hypotheses into engineered

Qualifications

• U.S. Citizenship is a requirement for this role (you must be able to be staffed on Critical Industries work which includes Government, Defense, Aerospace, Utilities, etc.)
• Degree in Computer Science/Engineering, or equivalent experience
• 2+ 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)
• Willingness to travel as required

Responsibilities

• Build the foundational data infrastructure that powers cutting-edge AI applications leveraging LLMs, retrieval systems, workflows, and emerging agentic architectures.
• Design and maintain scalable data pipelines, manage secure data environments, and prepare data for AI-driven systems while collaborating with cross-functional teams and clients.
• Develop next-generation AI systems and grow as a technologist by working with diverse experts across industries.
• Design and build scalable, reproducible data components essential for machine learning, agentic, and autonomous AI systems; translate hypotheses into engineered features.
• Collaborate with Data Scientists, ML Engineers, and industry experts to deliver AI solutions; partner with clients to solve complex problems and drive business value.

Skills

Python

SQL

Data pipelines

Agentic AI

Generative AI

Machine Learning

Communication

Travel willingness

Education

Bachelor's degree in Computer Science/Engineering

Tools

LangChain

LangSmith

Langfuse

Terraform

dbt

Spark

Pandas

Databricks

Snowflake

BigQuery

SQL

AWS

Azure

GCP

Job description

McKinsey & Company seeks a data engineering professional to build foundational data infrastructure powering AI applications, including LLMs, retrieval systems, and agentic architectures. You will design scalable pipelines, secure data environments, and prepare data for AI-driven systems while collaborating with cross-functional teams and clients.

You will work with Data Scientists, ML Engineers, and industry experts to deliver end-to-end AI solutions, translate hypotheses into engineered