Analytics Engineer

Salary: £61,000 - 101,000 per year

Requirements:

• We require a Bachelors or Masters degree in Computer Science, Data Engineering, Mathematics, Statistics, Econometrics, or a related quantitative discipline.
• We require a minimum of 3 years of experience in a data engineering, analytics engineering, or BI development role.
• We require strong hands-on experience with Databricks or an equivalent enterprise data platform such as Azure Synapse, Microsoft Fabric, or Snowflake.
• We require proficiency in SQL and experience building and optimising semantic or data models at scale, including window functions and complex business logic.
• We require a solid understanding of data modelling principles, including star schema, dimensional modelling, and DAX.
• We require experience with Power BI and the Microsoft Power Platform, including building on top of central or curated data models.
• We require familiarity with data pipeline concepts, transformation logic, and ETL/ELT processes.
• We require a working understanding of how data assets are structured and prepared to support AI, machine learning, and agentic use cases.
• We require the ability to translate business logic such as cost models, scoring frameworks, and taxonomy-based classifications into structured, auditable transformations.
• We require a strong understanding of data governance, data quality, and documentation best practices.
• We require comfort operating with ambiguity as transformation asks evolve with the business case.
• We require excellent written and visual communication skills for work that feeds executive decks and capital-approval workbooks.
• We consider Microsoft Fabric Analytics Engineer Associate (DP-600) or Databricks certification desirable, and PL-300 or DP-203 advantageous.
• Experience working within an Agile or hybrid delivery model is an advantage.
• We require fluency in English; additional European languages are an advantage.
• Experience supporting business cases, cost transformation, or restructuring programmes is nice to have.
• Exposure to workforce or organisational modelling, including headcount, resourcing, or cost-to-serve, is nice to have.
• Real estate or professional services domain experience is nice to have.
• Git-based workflows and code review habits are nice to have.

Responsibilities:

• We design, build, and maintain semantic models and curated datasets on the Databricks platform, ensuring they are performant, reusable, and aligned with governance standards.
• We act as the technical bridge between the central Data Engineering team and transformation or business stakeholders, translating ambiguous business questions into structured data models.
• We build and maintain automation and AI-readiness scoring frameworks, turning taxonomy and process data into structured, defensible metrics used in executive business cases.
• We produce the quantitative backbone of transformation business cases, including current-state baselines, savings and benefit tracking, scenario or what-if models, and before-and-after comparisons.
• We standardise datasets and business logic so transformation workstreams build consistently on a shared foundation rather than one-off extracts.
• We collaborate with the data engineering team on the design of upstream data assets, ensuring they meet the requirements of scalable, transformation-facing analytics.
• We contribute to data assets and pipelines structured to support AI, machine learning, and agentic workflows.
• We monitor and optimise the performance of data models and semantic layers, proactively identifying and resolving data quality issues before they reach executive reporting.
• We support platform adoption through documentation, standards, and knowledge transfer, contributing to best practices for data modelling, naming conventions, and dataset architecture.
• We participate in peer reviews of data models to ensure consistency and quality before release into business-critical decks and workbooks.

Technologies:

• AI
• Azure
• Backbone
• Databricks
• DAX
• ETL
• Fabric
• Git
• Support
• Machine Learning
• Microsoft Power Platform
• Power BI
• SQL
• Snowflake
• Cloud
• Agentic AI

More:

We are a Cushman & Wakefield team leading the data transformation of our EMEA and APAC business. Our mature lakehouse platform is built on Databricks and already runs the business end to end. We are now focused on putting agentic capability to work at real enterprise scale across a wide range of domains. This role sits between data engineering and the business, shaping semantic models, curated datasets, and reusable data assets that support leaders across EMEA and APAC. The position is based in London, United Kingdom, and is full time. We are committed to equity in employment and to a diverse, inclusive, and barrier-free workplace.

last updated 36 week of 2026