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# Data Engineer (contract)

- **Company:** Microsoft
- **Location:** Atherstone, UK
- **Salary:** £46,800.0 - £50,700.0
- **Contract:** Temporary contract
- **Skills:** Microsoft Azure, Code Review, Data Validation, Data Governance, Extract Transform Load (ETL), Software Debugging, Python (Programming Language), Standard Sql, Runbook, SQL Databases, Azure Data Factory, Apache Spark, Git, Software Coding, Data Pipelines, Databricks
- **Published:** June 17, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=e462fcfde8226492

## About the Role

Do you have experience in Unity?, * Strong SQL + Python hands-on experience

 * Working experience with Apache Spark
 * Experience in Azure Databricks + Azure Data Factory (ADF)
 * Experience building and maintaining data pipelines
 * Experience with Git (code reviews, PRs, collaboration)
 * Ability to debug pipeline failures and data issues
 * Experience working in Agile delivery teams

Desired Qualifications:

 * Exposure to Unity Catalog / data governance frameworks
 * Experience with Azure DevOps
 * Exposure to event-driven / telemetry data systems

## Description

This role is for a hands-on Data Engineer focused on building and improving data pipelines using SQL, Python, Spark, and Azure tools like Databricks and ADF. The individual will work under senior guidance to deliver reliable, high-quality ETL/ELT pipelines and support ongoing modernization efforts. They will also handle pipeline optimisation, data quality checks, and production issue troubleshooting. The role involves contributing to workflow improvements, implementing governance changes like Unity Catalog, and maintaining clear technical documentation. Overall, it requires strong coding skills, problem-solving ability, and experience working in agile, production environments., * Build and maintain data pipelines (ETL/ELT) using SQL, Python, Spark

 * Work on Databricks and Azure Data Factory (ADF) pipelines
 * Support Unity Catalog migration tasks
 * Improve existing pipelines (performance, reliability, efficiency)
 * Add data quality checks and monitoring alerts
 * Troubleshoot pipeline failures and production issues
 * Follow existing workflow/orchestration standards
 * Write documentation, runbooks, and operational notes


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