Supports the development, optimization, and maintenance of Cushman & Wakefield’s commercial real estate (CRE) forecasting infrastructure across the Americas.
This role is focused on engineering robust data pipelines, automating model workflows, and ensuring the integrity and scalability of forecasting systems.
Operate as a self-sufficient data practitioner, capable of independently delivering data solutions or working side-by-side with technology teams to ensure alignment and production readiness of QIG capabilities on an iterative basis.
Works closely with senior economists, analytics leads, and technical teams to deliver high-quality, production-ready data solutions that underpin the firm’s House View and related analytical products.

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. Real estate experience a plus
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, contributing both independently and alongside engineering and analytical teams to deliver data solutions
Comfort working in iterative development settings, balancing hands-on execution with stakeholder collaboration and continuous feedback
Strong attention to detail and commitment to data quality
Excellent documentation, communication, and stakeholder management skills; comfortable operating as the technical translator between analytical domain experts and data engineering teams (when appropriate)
Excellent documentation and communication skills for technical audiences. Ability to participate meaningfully in engineering discussions
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