Supports the development, optimization, and maintenance of Cushman & Wakefield’s commercial real estate forecasting infrastructure
Engineer robust data pipelines, automate model workflows, ensuring integrity and scalability of forecasting systems
Operate as a self-sufficient data practitioner delivering data solutions
Works closely with senior economists, analytics leads, and technical teams to deliver high-quality, production-ready data solutions
Create and maintain documentation of synthetic data model architecture, data flows, and diagnostic procedures
Identify new data use cases for proprietary data and cleaning techniques for analytics applications
Contribute to the evolution of the QIG data infrastructure by identifying opportunities for efficiency gains, automation, and scalability

Bachelor’s or Master’s degree in Data Engineering, Data Science, Computer Science, Statistics, or a related technical field
5-7 years of experience in data engineering or a hybrid analytical/engineering role, preferably in a forecasting or analytics/production environment
Strong proficiency in Python/R, SQL, Databricks, Delta Lake and data pipeline frameworks (e.g., medallion architecture)
Experience with time series data, econometric / data science modeling workflows, and automation tools
Familiarity with cloud platforms (e.g., Azure, AWS) and version control systems
Demonstrated ability to operate in a collaborative, cross-functional environment
Comfort working in iterative development settings
Strong attention to detail and commitment to data quality
Excellent documentation, communication, and stakeholder management skills
Exposure to geospatial data concepts and CRE or macroeconomic datasets
Experience working with agile/scrum delivery models in a data and analytics context

Data Engineer

Key skills