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# Azure Data Engineer (Fabric & Cloud Data Platform)

- **Company:** Rackspace
- **Location:** UK
- **Experience:** Experienced
- **Salary:** £61,079.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Microsoft Azure, Batch Processing, Big Data, Cloud Engineering, Software Code Optimization, Information Engineering, Data Infrastructure, Data Integration, Software Debugging, DevOps, Python (Programming Language), Key Management, Log Analysis, Role-Based Access Control, Azure Data Lake, SQL Stored Procedures, SQL Databases, Systems Integration, Transact-SQL, Data Logging, Data Processing, Cloud Platform System, Azure Data Factory, Cloud Monitoring, Apache Spark, Git, Microsoft Fabric, Data Lakes, Pyspark, Bicep, Data Lakehouse, Terraform, Code Restructuring, Data Pipelines, Databricks
- **Published:** October 21, 2026
- **Apply:** https://www.totaljobs.com/job/cloud-data-engineer/rackspace-ltd-job108167787

## About the Role

* Cloud Platform & Fabric: 3+ years of dedicated hands-on experience in Microsoft Azure, with practical exposure to the Microsoft Fabric ecosystem (OneLake, Data Factory, Fabric Lakehouse).
 * Big Data Processing: Deep technical expertise with Apache Spark / Azure Databricks for distributed compute, memory optimization, and Delta Lake formats.
 * Data Engineering Languages: Advanced proficiency in SQL (T-SQL, SparkSQL) and Python (PySpark).
 * Security & Compliance: Direct experience implementing security controls for sensitive or identifiable data, utilizing Azure Key Vault, Azure Monitor, Log Analytics, and Microsoft Purview.
 * Data Integration: Proven track record of migrating, refactoring, or connecting legacy SQL/stored procedure logic and existing pipelines into modern cloud formats.

Preferred Qualifications & Certifications

 * Certifications: Microsoft Certified: Azure Data Engineer Associate (DP-203) or Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) is highly advantageous.
 * DevOps/IaC: Experience deploying data infrastructure as code using Terraform, Bicep, and managing deployments via Git/Azure DevOps.

Soft Skills

 * Exceptional analytical and root-cause debugging skills for distributed Spark jobs and multi-stage pipelines.
 * Ability to collaborate with compliance and security teams to validate data privacy baselines.
 * Strong communication skills to bridge technical delivery with business intelligence requirements.

## Description

We are seeking a highly skilled Azure Data Engineer to design, build, and maintain our enterprise data platform. In this role, you will implement a modern data lakehouse architecture capable of supporting raw and curated data zones, scalable batch processing, and future analytical workloads. You will play a key role in integrating our existing data pipelines, models, and transformation logic into a unified ecosystem built on Microsoft Fabric and Azure Databricks. A heavy emphasis is placed on enforcing strict encryption, comprehensive logging, auditing, and fine-grained access controls to secure identifiable data., * Data Lakehouse Architecture: Design and optimize a high-performance lakehouse pattern utilizing Microsoft Fabric OneLake and Azure Data Lake Storage Gen2 (ADLS Gen2).

 * Data Zone Management: Maintain architectural segregation between raw storage (immutability, landing) and curated data zones (cleansed, conformed business layers) while securely handling identifiable data.
 * Scalable Data Processing: Develop, tune, and monitor large-scale batch processing pipelines using Azure Databricks (Spark-based processing) and PySpark.
 * Orchestration & Workflow: Build automated data workflows using Microsoft Fabric Data Factory and Azure Data Factory to move data efficiently across the platform.
 * Legacy & Model Integration: Ensure seamless integration with existing data pipelines, legacy data models, and established transformation logic without breaking downstream analytical operations.
 * Platform Security & Governance: Implement enterprise-grade security baselines including data-at-rest/in-transit encryption, structured logging, automated audit trails, and strict access controls (RBAC, ABAC, and row/column-level security).
 * Future-Proof Analytics: Structure data foundations to readily support future analytical workloads, advanced reporting, and data science use cases.