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# Lead Data Engineer

- **Company:** Harnham
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £90,000.0 - £120,000.0
- **Contract:** Permanent contract
- **Skills:** Continuous Integration, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Microsoft Software, SQL Azure, Operational Databases, Azure Data Lake, Data Ingestion, Azure Data Factory, Microsoft Fabric, Software Version Control, Data Pipelines
- **Published:** September 2, 2026
- **Apply:** https://www.reed.co.uk/jobs/lead-data-engineer/57296855

## About the Role

The successful Lead Data Engineer will have the following skills and experience:

 * Strong commercial experience building and supporting production data platforms.
 * Expertise with Azure Data Factory and Azure SQL.
 * Experience developing ETL or ELT pipelines and transformation logic in production environments.
 * Knowledge of data quality frameworks, validation processes, and monitoring.
 * Experience implementing layered or medallion-style data architectures.
 * Understanding of CI/CD, source control, and infrastructure-as-code approaches.
 * Working knowledge of wider Azure data technologies such as Microsoft Fabric, Synapse, OneLake, or Azure Data Lake.
 * Experience with modern transformation frameworks such as dbt or similar tooling.
 * Strong communication skills with the ability to explain technical concepts to a range of stakeholders.

## Description

As a Lead Data Engineer, you will own and shape a modern Azure data platform, combining hands-on engineering with architectural leadership in a business where data is central to growth and innovation.

Specifically, you can expect to be involved in the following:

 * Lead the design, build, and enhancement of a modern Azure data platform.
 * Develop scalable ETL and ELT pipelines using Azure Data Factory and Azure SQL.
 * Implement robust data quality, validation, and monitoring frameworks.
 * Deliver a layered data architecture that supports analytics, reporting, and operational applications.
 * Modernise data ingestion processes using native Microsoft technologies.
 * Build historical and time-series data models that provide deeper business insights.
 * Implement infrastructure and deployment processes using source control and infrastructure-as-code principles.
 * Establish engineering standards, documentation, and governance practices.
 * Provide technical leadership and support the growth of the wider data engineering capability.


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