Data Engineer – Foundational AI Platform & Greenfield Data Transformation

About the Company

Our client is a global enterprise involved in supply chain, shipping, trading, and distribution sectors. Operating across international borders with a complex data environment, the business is embarking on a significant data and AI transformation project. This role offers the unique chance to contribute from the ground up to a greenfield development, establishing reliable data infrastructures that empower company-wide analytics, business intelligence capabilities, and future artificial intelligence applications. Operating within a newly created AI team, the Data Engineer will collaborate closely with senior leaders across commercial, operational, financial, and technical functions.

Key Responsibilities

• Construct and manage robust data ingestion workflows that consolidate operational, transactional, commercial, and third-party data into a unified cloud-based data platform.
• Architect and implement dependable ETL and ELT processes that span raw, refined, and curated datasets.
• Develop scalable data modeling solutions to facilitate reporting, analytics, semantic layer building, and support AI-powered data retrieval.
• Partner effectively with business units to map source systems, define critical data elements, and set trustworthy business metrics.
• Contribute to creating an enterprise-wide, governed data foundation emphasizing data quality, access management, lineage tracking, cataloging, and documentation.
• Adopt and apply best practices related to data classification, role-based access controls, and row/column-level security measures.
• Continuously monitor pipeline performance, ensure data integrity, reliability, and optimize cloud infrastructure costs.
• Assist early-stage AI and analytics projects including document intelligence, knowledge management, and secure query mechanisms.
• Influence engineering standards, development methodology, and platform architecture as the data function evolves.

Candidate Requirements

• A minimum of 5 years of practical data engineering experience or equivalent roles building and managing live data pipelines.
• Proficient in advanced SQL with experience handling complex operational or transactional datasets.
• Hands-on expertise with Azure data services such as Azure Data Factory, Azure Synapse Analytics, Azure Data Lake, or Microsoft Fabric.
• Experience designing and operationalizing ETL or ELT pipelines integrating real-world source data into cloud platforms or data lakehouses.
• Working knowledge of Python programming, ideally with exposure to Spark or PySpark for large-scale data transformations.
• Sound understanding of dimensional modeling techniques, including star schema design, semantic layers, or medallion architecture.
• Familiarity with code versioning systems (e.g., Git), continuous integration/continuous deployment practices, and engineering best practices for data workflows.
• Comfort working in startup-like, greenfield environments where frameworks and standards are in the process of being developed.
• A proactive and responsible approach to monitoring, diagnosing, and enhancing data pipelines.
• Capability to engage directly with business stakeholders, translating their requirements into actionable data solutions.
• Industry experience in energy, commodities, shipping, logistics, supply chain, trading, or financial services is a plus.
• Knowledge or exposure to Microsoft Purview, Power BI, data governance, data catalogues, API-driven data access, or AI-assisted analytics is advantageous.