포지션 상세
As a Data Engineer I, you will build the foundational data infrastructure that powers cutting-edge AI applications leveraging LLMs, retrieval systems, workflows, and emerging agentic architectures. You will 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. You’ll tackle real-world challenges by contributing to the development of next-generation AI systems and grow as a technologist by working alongside diverse experts across industries.
• 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
• Design and build the 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
• Learn to translate simple hypotheses into engineered features, manage secure data environments, and contribute to R&D initiatives focused on innovating and scaling next-generation AI capabilities
• Collaborate across McKinsey's QuantumBlack and Labs teams to develop innovative AI capabilities and scalable enterprise solutions
• Help build robust data foundations for scalable, production-ready AI systems
• Work in cross-functional Agile teams, collaborating closely with Data Scientists, Machine Learning Engineers, and industry experts to deliver AI solutions
• Partner with clients from data owners to C-level executives to help solve complex problems that drive tangible business value
• 0-2+ years' 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
• Strong communication skills, both verbal and written, in English and local office language(s)
• Exceptional time management in a complex and largely autonomous work environment
• Willingness to learn quickly and adapt to different project situations and tech stacks
주요업무
• 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
• Design and build the 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
• Learn to translate simple hypotheses into engineered features, manage secure data environments, and contribute to R&D initiatives focused on innovating and scaling next-generation AI capabilities
• Collaborate across McKinsey's QuantumBlack and Labs teams to develop innovative AI capabilities and scalable enterprise solutions
• Help build robust data foundations for scalable, production-ready AI systems
• Work in cross-functional Agile teams, collaborating closely with Data Scientists, Machine Learning Engineers, and industry experts to deliver AI solutions
• Partner with clients from data owners to C-level executives to help solve complex problems that drive tangible business value
자격요건
• Degree in Computer Science/Engineering, or equivalent experience• 0-2+ years' 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
• Strong communication skills, both verbal and written, in English and local office language(s)
• Exceptional time management in a complex and largely autonomous work environment
• Willingness to learn quickly and adapt to different project situations and tech stacks
