GRIT

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
<ul>
<li>Build and maintain data ingestion pipelines from operational, transactional, business and third-party data sources into a central cloud data platform.</li>
<li>Design and develop reliable ETL/ELT pipelines across raw, cleansed and curated data layers.</li>
<li>Create scalable data models to support reporting, analytics, semantic layers and future AI-enabled data access.</li>
<li>Work closely with business teams to understand source systems, define key data entities, and establish trusted business metrics.</li>
<li>Support the development of a governed, enterprise-wide data foundation covering data quality, access control, lineage, cataloguing and documentation.</li>
<li>Apply best practices around data classification, role-based access, and row/column-level security.</li>
<li>Monitor pipeline performance, data quality, reliability and cloud cost on an ongoing basis.</li>
<li>Support early AI and analytics initiatives, including document intelligence, knowledge retrieval, and governed query access.</li>
<li>Help shape engineering standards, development practices and platform structure as the data function scales.</li>
</ul>
Requirements
<ul>
<li>5+ years’ experience in data engineering or equivalent hands-on experience building and operating production data pipelines.</li>
<li>Advanced SQL skills, with strong experience working with complex operational or transactional datasets.</li>
<li>Hands-on experience with Azure data services, ideally Azure Data Factory, Azure Synapse, Azure Data Lake, or Microsoft Fabric.</li>
<li>Experience building ETL/ELT pipelines from real-world source systems into a cloud data platform or data lakehouse.</li>
<li>Working proficiency in Python, with exposure to Spark or PySpark for larger-scale transformations.</li>
<li>Good understanding of dimensional modelling, star schema design, semantic layers, or medallion architecture.</li>
<li>Familiarity with Git, version control, CI/CD practices, and engineering standards for data pipelines.</li>
<li>Comfortable working in a greenfield environment where processes, standards and platform design are still being built.</li>
<li>Strong ownership mindset, with the ability to proactively monitor, troubleshoot and improve data pipelines.</li>
<li>Ability to work directly with business stakeholders and translate commercial or operational requirements into practical data solutions.</li>
<li>Experience in energy, commodities, shipping, logistics, supply chain, trading, or financial services would be advantageous.</li>
<li>Exposure to Microsoft Purview, Power BI, data governance, data catalogues, API-led data access, or AI-enabled analytics would be a plus.</li>
</ul>

Data Engineer [Foundational AI Platform Build | Greenfield Data Transformation]

Data Engineer [Foundational AI Platform Build | Greenfield Data Transformation]

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