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# Data Engineer

- **Company:** Cognizant
- **Location:** London, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Microsoft Azure, Continuous Integration, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Transformation, Software Debugging, SQL Azure, Performance Tuning, Query Optimization, Azure Data Lake, SQL Stored Procedures, SQL Databases, Data Streaming, Azure Data Factory, Sql Optimization, Indexer, Git, Software Version Control, Data Pipelines, Databricks
- **Published:** September 23, 2026
- **Apply:** https://dejobs.org/x/x/34C2C70C4828400C86FD3E21BB29DDFB/job/

## About the Role

* Strong hands-on experience with SQL (complex joins, window functions, query optimization, indexing, stored procedures)
 * 4+ years of experience with Azure Data Factory (ADF) - pipelines, mapping data flows, triggers, linked services, integration runtimes
 * Solid understanding of Data Engineering principles
 * Strong working knowledge of the Azure data ecosystem: Azure Data Lake Storage (ADLS Gen2), Azure SQL Database
 * Experience with performance tuning and troubleshooting pipeline/data issues
 * Experience with version control (Git) and CI/CD pipelines (Azure DevOps preferred)
 * Understanding of data governance, security, and compliance practices
 * Strong analytical, debugging, and problem-solving skills

Preferred/Nice-to-Have Skills

 * Experience with Databricks
 * Familiarity with real-time/streaming data
 * Microsoft Azure Data Engineer Associate (DP-203) certification

## Description

* Design, develop, and maintain robust, scalable ETL/ELT pipelines using Azure Data Factory (ADF)
 * Optimizing SQL queries, stored procedures, and functions for data transformation and performance tuning
 * Own end-to-end delivery of data engineering tasks - from requirement gathering to deployment and support
 * Mentor junior data engineers and review their code/pipeline designs
 * Implement data quality checks, validation frameworks, and error-handling mechanisms within pipelines
 * Integrate data from structured/unstructured sources (APIs, flat files)
 * Collaborate with data analysts, and business stakeholders to translate requirements into technical solutions
 * Contribute to CI/CD implementation for data pipeline deployments
 * Maintain technical documentation for pipelines, data flows, and architecture


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