Analytics Engineer

Cushman & Wakefield; City of Westminster; On-site; GBP 60,000 - 90,000; Full time

Job summary

Cushman & Wakefield seeks an Analytics Engineer to join the Data & Analytics team across EMEA and APAC. You will design semantic models, curate datasets, and bridge data engineering with business stakeholders to drive enterprise-scale analytics and AI-readiness.

You will build scalable data assets on Databricks, leverage SQL and Power BI, and contribute to governance, documentation, and robust data pipelines. Strong communication and problem-solving are essential.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Mathematics, Statistics, Econometrics, or a related quantitative discipline.
• Minimum 3 years of experience in data engineering, analytics engineering, or BI development.
• Strong hands-on experience with Databricks or an 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 the Microsoft Power Platform, including building on top of central/curated data models.
• Familiarity with data pipeline concepts, transformation logic, and ETL/ELT processes.
• Working understanding of how data assets are structured to support AI, ML, and agentic use cases.
• Comfortable translating business logic into structured, auditable transformations.
• Strong understanding of data governance, data quality, and documentation best practices.

Responsibilities

• 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 upstream data assets, ensuring scalability and relevance for analytics.
• Contribute to data assets and pipelines structured to support AI, ML, and agentic workflows.
• Monitor and optimise the performance of data models and semantic layers, addressing data quality issues before executive reporting.
• Support platform adoption through documentation, standards, and knowledge transfer, including data modelling conventions and dataset architecture.
• Participate in peer reviews of data models to ensure consistency and quality before release into business-critical decks.

Skills

SQL

Data modelling

Power BI

Stakeholder communication

Ambiguity handling

Education

Quantitative degree

Tools

Databricks

Azure Synapse

Snowflake

Microsoft Fabric

Power Platform

Job description

We lead the data transformation of our EMEA and APAC business, and we are looking for an Analytics Engineer to join us. Our lakehouse platform is mature and well established. It is built on Databricks and already runs the business end to end. What comes next is the harder part, putting agentic capability to work at real enterprise scale and across a wide range of domains. You will take on problems nobody has written the playbook for, make calls that matter, and build depth across several domains early enough to shape where the platform goes next. Very few organisations in our sector have started this work, so you will pick up a skill set you would struggle to find anywhere else in this industry. You will sit between data engineering and the business. That means turning commercial questions into semantic models, curated datasets and reusable data assets that leaders across EMEA and APAC rely on, working with our engineers on the upstream design that keeps those assets scaling, and getting them ready for agentic and AI-led delivery. If you want work that carries real weight, we would like to hear from you. Responsibilities

• 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 (cost optimisation, resourcing, margin analysis) build consistently on a shared foundation rather than one-off extracts.
• 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 that are structured to support AI, machine learning, and agentic workflows - increasingly the default mode of delivery rather than a side project.
• Monitor and optimise the performance of data models and semantic layers, proactively identifying and resolving data quality issues before they reach executive reporting.
• Support platform adoption through documentation, standards, and knowledge transfer, contributing to best practices for data modelling, naming conventions, 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 a related quantitative discipline.
• Minimum 3 years of experience in a data engineering, analytics engineering, or BI development role.
• Strong hands-on experience with Databricks or an 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 the Microsoft Power Platform, including building on top of central/curated data models.
• Familiarity with data pipeline concepts, transformation logic, and ETL/ELT processes.
• Working understanding of how data assets are structured and prepared to support AI, machine learning, and agentic use cases - this is now a core expectation of the role, not a nice-to-have.
• Comfortable translating business logic (cost models, scoring frameworks, taxonomy-based classifications) into structured, auditable transformations.
• Strong understanding of data governance, data quality, and documentation best practices.
• Comfortable operating with ambiguity - transformation asks arrive half-formed and evolve as the business case develops.
• Excellent written and visual communication - you'll be producing numbers that go straight into executive decks and capital-approval workbooks.
• Microsoft Fabric Analytics Engineer Associate (DP-600) or Databricks certification desirable; PL-300 or DP-203 advantageous.
• Experience working within an Agile or hybrid delivery model is an advantage.
• Fluent in English; additional European languages an advantage.

Nice to have

• Experience supporting business cases, cost transformation, or restructuring programmes.
• Exposure to workforce/organisational modelling (headcount, resourcing, cost-to-serve).
• Real estate or professional services domain experience.
• Git-based workflows and code review habits.