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# Data Engineer

- **Company:** Harnham
- **Location:** London, UK
- **Salary:** £35,000.0 - £40,000.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Cloud Computing, Information Engineering, Extract Transform Load (ETL), Data Migration, Data Systems, Python (Programming Language), Software Product Management, Pandas, Pyspark, Infrastructure Automation Frameworks, Qlikview, Cloud Migration, Terraform, Data Pipelines, Databricks
- **Published:** August 30, 2026
- **Apply:** https://www.collegerecruiter.com/job/2815467697-data-engineer

## About the Role

* Strong experience with Azure and Databricks environments
 * Advanced Python skills for data engineering (pandas, PySpark)
 * Proficiency in designing and maintaining ETL pipelines
 * Experience with Terraform for infrastructure automation
 * Track record of working on cloud migration projects, especially Azure to Databricks
 * Comfortable working onsite in London 2 days/week and engaging cross-functionally
 * Strong communication and problem-solving abilities

NICE TO HAVES

 * Experience with Qlik or other data visualisation tools
 * Exposure to AI product integration or readiness projects

## Description

You'll join a skilled and collaborative analytics team of eight, working closely with senior leadership. The team has already laid the foundations for a modern data platform using Azure and Databricks and is now focused on building out scalable ETL processes, integrating AI tools, and delivering bespoke analytics solutions across the organisation., As a Data Engineer, you'll play a pivotal role in designing and implementing robust data pipelines, supporting the migration from legacy Azure systems to Databricks, and working closely with stakeholders to deliver tailored data solutions. This role combines hands-on development with collaborative architecture design, and offers the opportunity to contribute to AI readiness within a fast-paced business., * Develop and maintain ETL pipelines, including manual and semi-manual data loads

 * Connect and integrate diverse data sources across cloud platforms
 * Collaborate with analytics and design teams to create bespoke, scalable data solutions
 * Support data migration efforts from Azure to Databricks
 * Use Terraform to manage and deploy cloud infrastructure
 * Build robust data workflows in Python (e.g., pandas, PySpark)
 * Ensure the platform is scalable, efficient, and ready for future AI use cases


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