Data Engineer: AI‑Ready Pipelines on Azure (Hybrid)

HotSchedules; Macclesfield; Presencial; GBP 55.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Holidays and time off

Hybrid working environment

Laptop and equipment

Healthcare expense claims

Certifications and growth

Annual meet ups

Pension and life insurance

Cycle to work scheme

Salary advance tools

Descripción de la vacante

Fourth LTD is seeking a Data Engineer within the Enterprise Data & Intelligence team to design, build, and maintain scalable data pipelines on our Azure-based cloud platform. You will translate business requirements into reliable data solutions and collaborate with analytics engineers and stakeholders across FP&A, Commercial, Product, and AI teams.

The role emphasizes data quality, governance, and iterative improvement, with an eye toward AI-powered analytics and reproducible data assets.

Formación

• 3+ years in data engineering or a closely related role.
• Bachelor’s degree in Computer Science, Data Engineering, or a related field.

Responsabilidades

• Build, maintain, and optimise ETL/ELT pipelines (batch and near-real-time) on Azure Data Cloud Platform.
• Develop and refine data models to support BI reporting, analytics, and ML/AI use cases.
• Write efficient, well-documented T-SQL and PySpark code following team standards.
• Contribute to data governance: lineage tracking, metadata management, and quality controls.

Conocimientos

ETL/ELT pipelines

SQL & PySpark

Azure Data Factory

Data modelling

CI/CD for data

AI readiness

Educación

Bachelor’s degree in CS or related field

Herramientas

Azure Data Factory

Microsoft Fabric

Azure Data Lake

SSMS

Azure Storage Explorer

Git

Descripción del empleo

Fourth LTD is seeking a Data Engineer within the Enterprise Data & Intelligence team to design, build, and maintain scalable data pipelines on our Azure-based cloud platform. You will translate business requirements into reliable data solutions and collaborate with analytics engineers and stakeholders across FP&A, Commercial, Product, and AI teams.

The role emphasizes data quality, governance, and iterative improvement, with an eye toward AI-powered analytics and reproducible data assets.