Analytics Engineer: AI-Driven Data at Enterprise Scale

Cushman & Wakefield

Greater London

Hybrid

GBP 70,000 - 120,000

Full time

8 days ago

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Job summary

Cushman & Wakefield is seeking an Analytics Engineer to join our data platform team. You will design semantic models and curated datasets on Databricks, bridging data engineering and business stakeholders across EMEA and APAC to enable agentic AI delivery.

You will turn business questions into scalable data assets, collaborate with engineers on upstream design, and build dashboards and governance frameworks that power executive decision making. Fluency in English is required.

Qualifications

Bachelor’s or Master’s degree in a quantitative field.
Minimum 3 years in data engineering/analytics/BI.
Strong hands-on with Databricks or equivalent platform.
Proficiency in SQL and building semantic/data models at scale.
Solid data modelling knowledge – star schema, dimensional modelling, DAX.
Experience with Power BI and central data models.
Familiarity with ETL/ELT and data pipelines.
Experience supporting AI/agentic use cases and governance.
Ability to operate with ambiguity and communicate clearly.
Certifications in Databricks or Microsoft Fabric are desirable.
Experience in Agile or hybrid delivery is a plus.
Fluent in English; other European languages advantageous.

Responsibilities

Design, build, and maintain semantic models and curated datasets on the Databricks platform.
Act as the bridge between Data Engineering and transformation stakeholders.
Develop automation/AI-readiness scoring frameworks for executive business cases.
Produce baselines, savings tracking, and what-if analyses for transformation programs.
Standardise datasets and business logic for scalable transformation workstreams.
Collaborate on upstream data assets design to support scalability.
Contribute to AI/machine learning ready data assets and pipelines.
Monitor and optimise data model performance and data quality.
Support platform adoption through documentation and best practices.
Participate in peer reviews of data models before release.