Your Impact

You will be based at one of our Greater China offices and be part of our global Data Engineering community, working on cross-functional Agile project teams alongside Data Scientists, Machine Learning Engineers, other Data Engineers, Project Managers, and industry experts.

As a Data Engineer, you’ll help to build and maintain the technical platform for advanced analytics engagements, spanning data science and data engineering work; design and build data pipelines for machine learning that are robust, modular, scalable, deployable, reproducible, and versioned; create and manage data environments and ensure information security standards are maintained at all times; understand clients data landscape and assess data quality; map data fields to hypotheses and curate, wrangle, and prepare data for use in advanced analytics models. You will have the opportunity to contribute to R&D projects and internal asset development; and contribute to cross-functional problem-solving sessions with your team and our clients, from data owners and users to C-level executives, to address their needs and build impactful analytics solutions.

You will work hand-in-hand with our clients, from data owners, users, and fellow engineers to C-level executives. You will be a highly-collaborative individual who wants to solve problems that drive business value. You have a strong sense of ownership and enjoy hands-on technical work. Our values resonate with yours.

Only at McKinsey

Work on real-world, high-impact projects across a variety of industries– Have the opportunity to collaborate with QB/Labs teams and build complex and innovative ML systems to accelerate our work in AI – and help solve business problems at speed and scale.

Experience the best environment to grow as a technologist and a leader– Develop a sought-after perspective connecting technology and business value by working on real-life problems across a variety of industries and technical challenges to serve our clients on their changing needs.

Be surrounded by inspiring individuals as part of diverse multidisciplinary teams - Develop a holistic perspective of AI by partnering with the best design, technical, and business talent in the world as your team members.

Our Tech Stack

While we advocate for using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, our own open-source data pipelining framework called Kedro, Dask/RAPIDS, container technologies such as Docker and Kubernetes, cloud solutions such as AWS, GCP, and Azure, and more.

#LI-MP

Your qualifications and skills

Degree in computer science, engineering, mathematics, or equivalent experience
Ability to write clean, maintainable, scalable and robust code in an object-oriented language, e.g., Python, Scala, Java, in a professional setting
1+ years’ experience building data pipelines in a professional setting would be considered a plus
Proven experience building data pipelines in production for advanced analytics use cases
Experience working across structured, semi-structured and unstructured data
Familiarity with analytics libraries (e.g. pandas, numpy, matplotlib), data pipeline framework (e.g Airflow, Kedro, AzureML), one of distributed computing frameworks (e.g. Spark, Dask), experience with cloud platforms (e.g. AWS, Azure, GCP) is a plus
Exposure to software engineering concepts and best practices, inc. DevOps, DataOps and MLOps would be considered a plus
Experience deploying containerized application to Kubernetes is a plus
Good presentation and communication skills, with the ability to communicate complex ideas effectively - both verbally and in writing - in English and Chinese
Willingness to travel

Data Engineer - QuantumBlack, AI by McKinsey