Data Engineer I - QuantumBlack, AI by McKinsey

This role is for a Data Engineer I within QuantumBlack, AI by McKinsey. You will build the foundational data infrastructure that powers cutting-edge AI applications, leveraging LLMs, retrieval systems, workflows, and emerging agentic architectures.

What You'll Do

• Design and maintain scalable data pipelines.
• Manage secure data environments.
• Prepare data for AI-driven systems.
• Collaborate with cross-functional teams and clients.
• Tackle real-world challenges by contributing to the development of next-generation AI systems.
• Grow as a technologist by working alongside diverse experts across industries.
• Design and build scalable, reproducible data components essential for machine learning, agentic, and autonomous AI systems.
• Assess data landscapes, apply data quality fundamentals, and prepare data for AI solutions.
• Translate simple hypotheses into engineered features.
• Contribute to R&D initiatives focused on innovating and scaling next-generation AI capabilities.
• Help solve complex and high-impact challenges facing clients across industries.
• Develop innovative AI capabilities and scalable enterprise solutions.
• Build robust data foundations for scalable, production-ready AI systems.
• Accelerate AI adoption and solve complex business problems at scale.
• Work in cross-functional Agile teams, collaborating closely with Data Scientists, Machine Learning Engineers, and industry experts.
• Partner with clients from data owners to C-level executives to solve complex problems that drive tangible business value.
• Develop expertise at the intersection of technology and business by tackling diverse challenges in the evolving AI landscape.
• Gain a holistic understanding of how data engineering enables advanced AI.

Your Qualifications and Skills

• Degree in Computer Science/Engineering, or equivalent experience.
• Experience in a data-focused role (internships, academic projects).
• Ability to write clean, well-documented code in a modern programming language, with a strong preference for Python and SQL.
• Interest in or exposure to data engineering in the context of Agentic AI, Generative AI, Machine Learning, or Business Intelligence across different types of data formats (structured vs unstructured) and processing methods (streaming vs batch).
• Familiarity with commonly used data platforms (Databricks, Snowflake, BigQuery, PSQL, etc.), cloud platforms (AWS, Azure, GCP) and engineering tools (Pandas, Spark, dbt, LangChain, etc.).
• Exposure to modern software development practices, including version control (Git) and foundational concepts in DevOps and MLOps/LLMOps and CI/CD principles.
• Exceptional time management in a complex and largely autonomous work environment.
• Willingness to learn quickly and adapt to different project situations and tech stacks.
• Experience using coding agents (Cursor, Claude Code, Codex, etc.) is a plus.
• Strong communication skills, both verbal and written, in English.