Company Description
Our client is a globalbusiness operating across supply, shipping, trading and distribution. With a highly international footprint and complex operational data environment, the business is now investing in a major data and AI transformation programme.
This is an opportunity to join at the beginning of a greenfield build, helping create the governed data foundations that will enable enterprise-wide analytics, business intelligence and future AI use cases. The role sits within a newly formed AI function and will work closely with senior stakeholders across commercial, operations, finance and technology.
For someone who wants more than a standard data engineering role, this is a chance to become one of the first hands-on builders of a modern data platform in a global, asset-heavy industry.
Responsibilities
Build and maintain data ingestion pipelines from operational, transactional, business and third-party data 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 to support reporting, analytics, semantic layers and future AI-enabled data access.
Work closely with business teams to understand source systems, define key data entities, and establish trusted business metrics.
Support the development of a governed, enterprise-wide data foundation covering data quality, access control, lineage, cataloguing and documentation.
Apply best practices around data classification, role-based access, and row/column-level security.
Monitor pipeline performance, data quality, reliability and cloud cost on an ongoing basis.
Support early AI and analytics initiatives, including document intelligence, knowledge retrieval, and governed query access.
Help shape engineering standards, development practices and platform structure as the data function scales.
Requirements
5+ years' experience in data engineering or equivalent hands-on experience building and operating production data pipelines.
Advanced SQL skills, with strong experience working with complex operational or transactional datasets.
Hands-on experience with Azure data services, ideally Azure Data Factory, Azure Synapse, Azure Data Lake, or Microsoft Fabric.
Experience building ETL/ELT pipelines from real-world source systems into a cloud data platform or data lakehouse.
Working proficiency in Python, with exposure to Spark or PySpark for larger-scale transformations.
Good understanding of dimensional modelling, star schema design, semantic layers, or medallion architecture.
Familiarity with Git, version control, CI/CD practices, and engineering standards for data pipelines.
Comfortable working in a greenfield environment where processes, standards and platform design are still being built.
Strong ownership mindset, with the ability to proactively monitor, troubleshoot and improve data pipelines.
Ability to work directly with business stakeholders and translate commercial or operational requirements into practical data solutions.
Experience in energy, commodities, shipping, logistics, supply chain, trading, or financial services would be advantageous.
Exposure to Microsoft Purview, Power BI, data governance, data catalogues, API-led data access, or AI-enabled analytics would be a plus.