Data Engineer - AI Platform & Data Transformation

About the role

A global business active in supply, shipping, trading and distribution is investing in a major data and AI transformation programme. This is a greenfield build within a newly formed AI function, creating the governed data foundations needed for enterprise analytics, business intelligence and future AI use cases. The role works closely with senior stakeholders across commercial, operations, finance and technology.

Key responsibilities

• Build and maintain data ingestion pipelines from operational, transactional, business and third-party sources into a central cloud data platform.
• Design and develop reliable ETL/ELT pipelines across raw, cleansed and curated data layers.
• Create scalable data models supporting reporting, analytics, semantic layers and future AI-enabled data access.
• Work with business teams to understand source systems, define key data entities and establish trusted business metrics.
• Support a governed data foundation covering data quality, access control, lineage, cataloguing and documentation.
• Apply data classification, role-based access and row/column-level security.
• Monitor pipeline performance, data quality, reliability and cloud cost.
• Support early AI and analytics initiatives such as document intelligence, knowledge retrieval and governed query access.
• Help shape engineering standards, development practices and platform structure as the data function grows.

Required profile

• 5+ years' experience in data engineering or equivalent hands-on experience building and operating production data pipelines.
• Experience building ETL/ELT pipelines from real-world source systems into a cloud data platform or data lakehouse.
• Comfortable working in a greenfield environment where processes, standards and platform design are still being built.
• Strong ownership mindset, proactive monitoring and troubleshooting of data pipelines.
• Ability to work directly with business stakeholders and turn their requirements into practical data solutions.

Required skills

• Advanced SQL with complex operational or transactional datasets.
• Azure data services, ideally Azure Data Factory, Azure Synapse, Azure Data Lake or Microsoft Fabric.
• Python; exposure to Spark or PySpark for large-scale transformations.
• Dimensional modelling, star schema, semantic layers or medallion architecture.
• Git, version control, CI/CD practices and data pipeline engineering standards.
• Advantageous: energy, commodities, shipping, logistics, supply chain, trading or financial services experience.
• Plus: Microsoft Purview, Power BI, data governance, data catalogues, API-led data access or AI-enabled analytics.