Analytics Engineer

Analytics Engineer

Farringdon, London/ HYBRID

UP TO £60,000

Overview

This is a standout opportunity to join a growing data function at an organisation where data is becoming central to how the business operates. You will step into a highly visible role with real influence, owning end to end delivery across data pipelines, modelling, and reporting while helping to shape standards and ways of working from an early stage.

The Company

They are a globally operating design and professional services organisation delivering complex projects across a wide range of sectors. Headquartered in London, the business works internationally and is known for its ambitious, design led approach. Data is increasingly important to how they manage operations, finance, and decision making, and the data capability is now being built with long term scale in mind.

The Role & Responsibilities

• Build, manage, and maintain end to end data pipelines using Azure-based tooling.
• Develop and evolve a lakehouse-style platform, with Databricks used primarily for transformation and modelling.
• Integrate data from core business systems such as finance and HR via APIs and structured feeds.
• Translate business requirements into robust, scalable data models using best practice approaches.
• Own the semantic layer and reporting layer in Power BI, enabling trusted self-serve analytics.
• Work closely with stakeholders across the business to define how data should be structured and used.
• Play a key role in shaping data standards, patterns, and foundations as the team grows.

Skills and Experience

• Strong commercial experience working in a cloud data environment, ideally Azure.
• Solid SQL capability and confidence building reliable transformation logic.
• Experience with data modelling concepts such as star schema, Kimball-style modelling, and semantic layers.
• Power BI experience across data modelling, DAX, and report development.
• Exposure to Databricks and lakehouse concepts, including incremental loads and snapshotting.
• Comfortable engaging with non-technical stakeholders and translating requirements into data solutions.