Data Engineer – Scalable AI Pipelines

About the role

As a Data Engineer you will design, build and maintain scalable data pipelines that feed advanced analytics and machine‑learning models for McKinsey’s QuantumBlack AI practice. You will work from our Zurich office alongside data scientists, ML engineers and industry experts to turn raw data into actionable insights for global clients.

Key responsibilities

• Design and implement reproducible, high‑performance data pipelines for machine‑learning workloads.
• Assess data landscapes, ensure data quality and prepare datasets for analytics models.
• Manage secure data environments and support cloud‑based data platforms (AWS, Azure, GCP).
• Collaborate with cross‑functional Agile teams, including Data Scientists and client stakeholders.
• Contribute to R&D projects, internal asset development and the evolution of QuantumBlack’s data engineering framework.

Required profile

• Degree in Computer Science, Engineering, Mathematics or equivalent experience.
• Up to 2 years of experience building data pipelines in a professional setting.
• Strong communication skills in German and English, with the ability to explain technical concepts to non‑technical audiences.

Required skills

• Programming: Python, Scala, Java.
• Data libraries: pandas, numpy, matplotlib.
• Distributed computing: Spark, Dask, PySpark.
• Cloud platforms: AWS, Azure, GCP.
• Orchestration and tooling: SQL, Airflow, Databricks, Kedro, Docker, Kubernetes.

What we offer

• Continuous learning through structured apprenticeship and coaching programs.
• A voice that matters – you can contribute ideas from day one.
• Global community of 100+ nationalities across 65+ countries.
• World‑class benefits and a competitive salary package.