Data Analytics Engineer

Job Title

Data Analytics Engineer

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.

Job Description

Key Responsibilities

Data Engineering & Pipeline Development

• Design, develop, maintain, and troubleshoot ETL/ELT pipelines that ingest, transform, validate, and deliver data from multiple sources into centralized data platforms.

• Build and optimize data processing workflows using Databricks or Microsoft Fabric to support scalable analytics and reporting requirements.

• Automate and improve recurring data processes to strengthen pipeline performance, reliability, and operational efficiency.

Data Modeling & Integration

• Develop and maintain structured data models, including dimensional models where appropriate, that support business intelligence, dashboards, reporting, and analytical use cases.

• Integrate and organize data from multiple source systems into accessible, consistent datasets designed for downstream consumption.

• Prepare and transform data to enable visualization, reporting, business analysis, and stakeholder decision-making.

Data Quality, Governance & Reliability

• Implement data validation, quality controls, governance practices, and documentation that improve the accuracy and reliability of analytical data.

• Identify and resolve data issues, inconsistencies, and pipeline failures that could affect reporting quality or stakeholder confidence.

• Document data structures, transformations, business rules, and technical processes to support transparency, maintainability, and consistent use.

Business Intelligence & Data Accessibility

• Develop datasets and data structures that enable dashboards, visualization, reporting, and other stakeholder-facing analytical solutions.

• Support dashboard and visualization development using Power BI or comparable business intelligence platforms.

• Improve how business users and analytics teams access and use trusted data for recurring reporting and decision support.

Stakeholder Partnership & Continuous Improvement

• Partner with client stakeholders, analytics teams, business leaders, and technical partners to gather requirements and translate business needs into technical solutions.

• Communicate technical concepts, data limitations, dependencies, and delivery progress clearly to technical and nontechnical stakeholders.

• Identify and implement opportunities to automate data workflows, reduce manual processes, improve maintainability, and support geospatial or location-intelligence requirements where applicable.

Qualifications

Must-Have Requirements

• Approximately 3–4+ years of relevant experience in data engineering, analytics engineering, business intelligence, or a closely related field.

• Hands-on experience with Databricks or Microsoft Fabric, including practical experience…