MENG Qiang

Qiang is a data engineer leading a team involved in building future data platforms across the entire fashion value chain from design and production to customer experience. Qiang has over 8 years of experience in creating enterprise analytics products applying Python, Spark, Airflow, and Cloud Services (AWS, GCP, and Databricks).

In the past, Qiang also shared his understanding of Data and AI in Global Summits (Data+AI Summit Europe 2020, Data Innovation Summit 2021, Pycon SE 2021, etc.). In addition, Qiang is a fashion lover and a part-time fashion designer.


5 Recipes to Fashionable Airflow Data Engineering Pipelines
MENG Qiang, Dahmane Sheikh, Grzegorz Skibinski

Live broadcast:

Apache Airflow has become one of the most popular data toolings. Due to its high
complexity, it could be challenging for all teams and companies. For example, how to
effectively construct an orchestrate architecture on diverse cloud platforms, how to
productively accelerate your engineering and machine learning workload at scale, and how
to smartly decouple your Python codebase for professional testing and easy maintenance.

Data Science, AI, and Machine Learning