Data Engineer


Company:	Reyan Technologies Ltd
Salary:	£36,000 - £42,000
Hours:	Full-time
Location:	Hounslow, TW4 6JQ
Working pattern:	On-site
Job type:	Permanent
Posting date:	31 Jul 2026
Closing date:	30 Aug 2026

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Summary

Job Description:

Reyan Technologies Ltd is looking for a skilled Data Engineer to join our team and support the design, development, and maintenance of data solutions that enable business insights. As a Data Engineer, you will be responsible for developing and maintaining ETL/ELT processes, cleansing and validating data, optimizing SQL queries, and building reliable data pipelines. You will work closely with developers, analysts, and business stakeholders to deliver scalable and high-quality data solutions while contributing new ideas to improve existing processes.

Job Role and Responsibilities:

• Build, maintain, and support data pipelines using Azure Data Factory and Snowflake.

• Develop and maintain ETL/ELT processes for integrating data from multiple source systems.

• Design and maintain data warehouse structures using star and snowflake schemas.

• Write, review, and optimise SQL queries, stored procedures, views, and functions.

• Perform data cleansing, validation, reconciliation, and quality checks to ensure accurate reporting.

• Support data migration, transformation, and integration activities across cloud and on-premises environments.

• Develop and maintain Azure Data Factory pipelines for data ingestion and transformation.

• Work with Azure Data Lake Storage, Azure SQL Database, and Azure Databricks to support data processing requirements.

• Assist in developing Spark-based data processing solutions where required.

• Collaborate with Business Analysts, Developers, and QA teams to understand business requirements and deliver data solutions.

• Support reporting and analytics teams by preparing reliable datasets for Power BI and other reporting tools.

• Monitor ETL jobs, troubleshoot failures, and optimise pipeline performance.

• Participate in Agile/Scrum ceremonies including sprint planning, stand-ups, reviews, and retrospectives.

• Contribute ideas for improving data engineering processes and automation.

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