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.

You will design and build the scalable, reproducible data components essential for machine learning, agentic, and autonomous AI systems. You’ll assess data landscapes, apply data quality fundamentals, and prepare data for AI solutions. Additionally, you’ll 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.

Your work will help solve some of the most complex and high-impact challenges facing clients across industries. Collaborating across McKinsey’s QuantumBlack, AI by McKinsey and QuantumBlack Labs teams, you’ll help develop innovative AI capabilities and scalable enterprise solutions. You’ll help build robust data foundations for scalable, production-ready AI systems. Your contributions will directly support the firm’s ability to accelerate AI adoption, solve complex business problems at scale, and enable clients to achieve meaningful, lasting impact.

You’ll be based in Germany as part of our global Data Engineering community. Working in cross-functional Agile teams, you’ll collaborate closely with Data Scientists, Machine Learning Engineers, and industry experts to deliver AI solutions. By partnering with clients—from data owners to C-level executives—you’ll help solve complex problems that drive tangible business value and begin to build your skills as a client-facing technologist.

You will grow at the forefront of AI, data engineering, and emerging agentic technologies. You’ll develop expertise at the intersection of technology and business by tackling diverse challenges in the evolving AI landscape. Working alongside multidisciplinary teams, you’ll gain a holistic understanding of how data engineering enables advanced AI while collaborating with leading AI and data experts in the industry.

• Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
• A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
• Global community: With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
• World-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family.

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 German and English