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# Lead Data Engineer

- **Company:** Harnham
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £70,000.0 - £85,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Data Architecture, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), DevOps, Programming Tools, Python (Programming Language), Machine Learning, Standard Sql, Cloud Platform System, Delivery Pipeline, Git, Data Delivery, Data Pipelines
- **Published:** August 14, 2026
- **Apply:** https://www.reed.co.uk/jobs/lead-data-engineer/57237352

## About the Role

* Commercial experience leading or managing Data Engineering teams.
 * Strong SQL and Python development skills.
 * Expertise building and maintaining scalable ETL and ELT pipelines.
 * Strong understanding of data modelling and modern data architecture principles.
 * Experience working with Azure, AWS and/or GCP cloud platforms.
 * Knowledge of Git, CI/CD, DevOps practices, and deployment workflows.
 * Experience improving engineering standards, processes, and team capabilities.
 * Ability to work effectively with technical and non-technical stakeholders.
 * Interest in AI-enabled engineering practices and modern data platforms.

## Description

As Lead Data Engineer, you will be responsible for establishing and leading the Data Engineering function while working closely with senior stakeholders across technology, product, and data teams., * Defining Data Engineering standards, processes, and best practices across the organisation.

 * Leading, mentoring, and developing a growing team of Data Engineers.
 * Designing and delivering scalable ETL and ELT pipelines across operational, analytical, and AI workloads.
 * Taking ownership of cloud-based data platforms and modern data architecture.
 * Driving improvements in data modelling, platform performance, and engineering quality.
 * Leading CI/CD practices, deployment processes, and engineering governance.
 * Collaborating with Data Science and AI teams to productionise machine learning outputs.
 * Identifying opportunities to leverage AI-enabled development tools across the engineering lifecycle.
 * Implementing monitoring, validation, and governance frameworks to ensure reliable data delivery.


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