Analytics Engineer

As Analytics Engineer at Cushman & Wakefield, you will turn commercial questions into semantic data models and curated datasets on a mature Databricks-based lakehouse. You will bridge central Data Engineering and business stakeholders to enable AI-ready delivery across EMEA and APAC. Your work shapes scalable data assets and analytics that powers enterprise decisions, with a focus on governance, performance, and measurable transformation impact. This role offers unique exposure to agentic-enabled analytics at scale in a leading real estate data platform.

• Design, build, and maintain semantic models and curated datasets on the Databricks platform, ensuring they are performant, reusable, and aligned with governance standards
• Act as the technical bridge between the central Data Engineering team and transformation/business stakeholders, translating ambiguous business questions into structured data models
• Build and maintain automation/AI-readiness scoring frameworks — turning taxonomy and process data into structured, defensible metrics used in executive business cases
• Produce the quantitative backbone of transformation business cases: current-state baselines, savings and benefit tracking, scenario/what-if models, and before/after comparisons
• Standardise datasets and business logic so that transformation workstreams build consistently on a shared foundation
• Collaborate with the data engineering team on the design of upstream data assets, ensuring they meet the requirements of scalable, transformation-facing analytics
• Contribute to data assets and pipelines structured to support AI, machine learning, and agentic workflows
• Monitor and optimise the performance of data models and semantic layers, resolving data quality issues before executive reporting
• Support platform adoption through documentation, standards, and knowledge transfer, contributing to best practices for data modelling and dataset architecture
• Participate in peer reviews of data models to ensure consistency and quality before release into business-critical decks and workbooks

• Bachelor's or Master's degree in Computer Science, Data Engineering, Mathematics, Statistics, Econometrics, or related quantitative discipline
• Minimum 3 years of experience in data engineering, analytics engineering, or BI development
• Strong hands-on experience with Databricks or equivalent enterprise data platform (Azure Synapse, Microsoft Fabric, Snowflake)
• Proficiency in SQL and experience building and optimising semantic or data models at scale, including window functions and complex business logic
• Solid understanding of data modelling principles — star schema, dimensional modelling, DAX
• Experience with Power BI and Microsoft Power Platform
• Familiarity with data pipeline concepts, ETL/ELT processes
• Experience supporting AI, machine learning, and agentic use cases in data assets
• Ability to translate business logic into structured, auditable transformations
• Strong understanding of data governance, data quality, and documentation best practices
• Comfortable operating with ambiguity and evolving business cases
• Excellent written and visual communication for executive decks

• strong communication
• ability to collaborate cross-functionally
• analytical mindset
• Databricks
• Azure Synapse
• Microsoft Fabric