Data Analytics Engineer

Location: Remote CW Site - USA - Flexible United States

Job Title

Job Description Summary

As the Data & Analytics Engineer, you will build and maintain scalable data pipelines, data models, and organized datasets that enable reliable analytics, reporting, and business decision-making across strategic client accounts. You will translate business and data requirements into durable technical solutions, with hands-on ownership of data ingestion, transformation, integration, quality, and preparation for downstream business intelligence and visualization.

As a member of Cushman & Wakefield’s Portfolio Advisory Group, the Data & Analytics Engineer will be dedicated to one or more of Cushman & Wakefield’s most dynamic client accounts. In this role, you will collaborate closely with peers and the account team to support a client's strategic real estate vision. The Data & Analytics Engineer will embody our team philosophy “Client First, Insight Always, Execution Excellence”, ensuring every deliverable reflects the team’s commitment to innovation, collaboration, and measurable impact.

This role will primarily

• Build and maintain scalable ETL/ELT pipelines and data structures that integrate information from multiple sources and make trusted data accessible for analytics and reporting.
• Transform, model, validate, and organize data so stakeholders can rely on consistent datasets for dashboards, visualization, reporting, and decision support.
• Translate client and business requirements into practical data engineering solutions using Databricks or Microsoft Fabric, SQL, and related technologies.
• Improve data accessibility, reliability, and delivery efficiency through automation, data quality controls, documentation, and technical process improvement.

Success in this role looks like

• Data pipelines consistently deliver reliable, accurate, and accessible datasets for downstream analytics and stakeholder-facing solutions.
• Data models and transformations support scalable reporting and reduce dependence on manual data preparation.
• Data quality controls and documentation improve confidence, consistency, and usability across analytics outputs.
• Business requirements are translated into maintainable technical solutions that stakeholders and analytics teams can effectively use.
• Key Responsibilities