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# Analytics Engineer

- **Company:** Zoopla
- **Location:** London, UK (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Automation of Tests, Continuous Integration, Data Warehousing, Power BI, Software Engineering, SQL Databases, Tableau (Software), Cloud Platform System, Software Version Control, Automation Anywhere, Databricks
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/senior-analytics-engineer-zoopla-9784964

## About the Role

* Has experience setting up & developing dbt architecture using CI/CD practices (essential)
 * Is highly proficient with SQL and data modelling best practices for optimising performance (essential)
 * Has previously worked with visualisation tools such as Tableau or PowerBI (essential)
 * Is confident collaborating and communicating with non-technical stakeholders (essential)
 * Has experience integrating dbt with other tools in the modern data stack (essential)
 * Is comfortable harnessing AI workflows to expedite development in a scalable, sustainable way (essential)
 * Has experience working with Databricks and AWS (Glue, Lambda, Step Functions) or similar cloud platform (desirable)

## Description

As a Senior Analytics Engineer in the Data Analytics team, you'll own and develop the data modelling infrastructure, architecture and pipelines powering a wide range of data initiatives and data products.

 * Own the analytics engineering workflow and architecture for the data modelling layer at the heart of the business.
 * Ensure orchestration runs successfully, troubleshooting errors in key pipelines and empowering others to do the same.
 * Effectively communicate and act as a liaison between highly technical and non-technical stakeholders, to translate business needs into engineering requirements.
 * Deliver, and enable others to build, well-defined, transformed, tested, documented, and code-reviewed data sets. Due to the high quality of this data and the associated documentation, business users are able to self-serve, analyse, and build products and services upon it, getting reliable, consistent results.
 * Proactively seek automation and optimisation opportunities to constantly improve and develop the transformation layer, and unify its usage across multiple teams.
 * Train, upskill and support others in dbt and data modelling best practices, applying software engineering practices to analytics code (version control, automated testing, continuous integration).


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