Analytics Engineer

Description

The team leads data transformation for the EMEA and APAC business and is hiring an Analytics Engineer.

The lakehouse platform is established and stable. It runs on Databricks and already supports the business from start to finish.

The next step is harder: applying agentic capability at enterprise scale across many different domains.

You will work on problems with no existing playbook, make consequential decisions, and build expertise in several domains early enough to shape the platform's direction.

Few organisations in the sector have started this work, so the skills you build will be hard to find elsewhere in the industry.

You will sit between data engineering and the business, converting commercial questions into semantic layers, datasets and reusable assets that EMEA and APAC leadership depend on.

You will also work with engineers on upstream design so those assets scale, and prepare them for agentic and AI-led delivery.

Responsibilities

• Build and maintain semantic models and curated datasets on Databricks that perform well, can be reused, and follow governance rules.
• Act as the link between the central data engineering group and transformation and business stakeholders, turning unclear questions into structured data models.
• Create and maintain automation and AI-readiness scoring frameworks that convert taxonomy and process data into structured, defensible metrics for executive business cases.
• Build the quantitative core of transformation business cases: current-state baselines, savings and benefit tracking, what-if scenarios and before-and-after comparisons.
• Standardise datasets and business logic so cost optimisation, resourcing and margin analysis workstreams share one foundation rather than relying on one-off extracts.
• Work with data engineers on the design of upstream assets so they meet the needs of scalable, transformation-facing analytics.
• Contribute to data assets and pipelines built to support AI, machine learning and agentic workflows, which are now the default way of delivering work.
• Monitor and tune data models and semantic layers, catching and fixing data quality issues before they reach executive reporting.
• Support platform adoption through documentation, standards and knowledge sharing on modelling practice, naming conventions and dataset architecture.
• Review peers' data models before they go into business-critical decks and workbooks.

Requirements

• Bachelor's or Master's degree in computer science, data engineering, mathematics, statistics, econometrics or a closely related quantitative field.
• At least 3 years in a data engineering, analytics engineering or BI development role.
• Hands-on experience with Databricks or a comparable enterprise data platform such as Azure Synapse, Microsoft Fabric or Snowflake.
• Strong SQL, and experience building and tuning semantic or data models at scale, including window functions and complex business logic.
• Solid grasp of data modelling principles: star schema, dimensional modelling and DAX.
• Experience with Power BI and the Microsoft Power Platform, building on central or curated data models.
• Familiarity with data pipelines, transformation logic and ETL or ELT processes.
• Working knowledge of how data assets are structured and prepared for AI, machine learning and agentic use cases. This is a core expectation of the role, not an optional extra.
• Able to turn business logic, such as cost models, scoring frameworks and taxonomy-based classifications, into structured and auditable transformations.
• Strong grasp of data governance, data quality and documentation practice.
• Comfortable working with ambiguity, since requests often arrive half-formed and change as the business case develops.
• Clear written and visual communication, as your numbers go straight into executive decks and capital-approval workbooks.
• Fluent in English.
• Git-based workflows and code review habits.
• Experience supporting business cases, cost transformation or restructuring programmes.
• Exposure to workforce or organisational modelling, such as headcount, resourcing and cost-to-serve.
• Real estate or professional services domain experience.
• Desirable: Microsoft Fabric Analytics Engineer Associate (DP-600) or a Databricks certification.
• Advantageous: PL-300 or DP-203 certification.
• Advantageous: experience working in an Agile or hybrid delivery model.
• Advantageous: additional European languages.

Conditions

The company is an equal opportunity and affirmative action employer and considers all qualified candidates regardless of any legally protected characteristic.