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# Analytics Engineer

- **Company:** Harnham
- **Location:** Greater London, UK
- **Contract:** Permanent contract
- **Skills:** Adaptable Database Systems, Data Analysis, Microsoft Azure, Power BI, SQL Databases, Workflow Management Systems, Azure Data Factory, Core Data, Api Design, Data Pipelines, Databricks
- **Published:** August 30, 2026
- **Apply:** https://www.collegerecruiter.com/job/2815452552-analytics-engineer

## About the Role

* Strong commercial experience with SQL.
 * Experienec working in MS Azure environment.
 * Hands-on experience working with Databricks, or a strong desire to deepen expertise in it.
 * Expert understanding of data modelling principles and semantic layer concepts.
 * Experience working with Azure Data Factory or similar orchestration tools.
 * Ability to translate business needs into logical, scalable data models.
 * Comfortable working across multiple data sources and handling pipeline design.
 * Experience with Power BI, including semantic models or Tabular Editor.
 * Curious, collaborative mindset with the ability to push back constructively and guide stakeholders.

## Description

This is a great opportunity to become the key Analytics Engineer within an early-stage data function, playing a defining role in how the organisation builds its lakehouse, models its core data, and shapes best practice for years to come. If you enjoy ownership, influence, and hands-on technical work across Databricks, data modelling, and pipeline development, this role offers genuine impact and long-term career growth., * Develop and maintain data pipelines across Databricks, Azure Data Factory and API-based integrations.

 * Work across transformation layers in Databricks, ensuring clean, well-modelled datasets.
 * Build and extend the Power BI data model, enabling reliable reporting and analytics.
 * Partner with stakeholders to understand requirements, validate assumptions and shape how data should be modelled.
 * Apply strong data modelling practices, including star schemas and Kimball methodologies.
 * Support the management of the lakehouse, including incremental loads, snapshotting and data quality.
 * Contribute to the relationship with Databricks to help expand adoption of its features.
 * Organise and maintain the existing platform, ensuring stability and clarity across data sources.


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