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Job Purpose and Impact

Key Accountabilities

• Understand the business question. Engage with Trading, Market Analysis, Origination, Commercial Operations, Supply Chain, Risk, and Product teams to define data requirements and enable informed business decisions.
• Investigate and understand data.Identify and assess relevant data sources to understand their availability, quality, and limitations before designing effective solutions.
• Build dependable data flows. Develop and maintain data pipelines using SQL, Python, and cloud data services to integrate data from multiple sources with reliable monitoring and error handling.
• Create resuable analytical datasets. Design and maintain standardized data models and documented datasets that align business definitions and support diverse analytical use cases.
• Own data quality by implementing automated checks, resolving discrepancies, and ensuring data accuracy, transparency, and alignment with business expectations.
• Apply analytical judgement. Explore trends, test hypotheses, and translate complex data into clear business insights through focused analysis and visualization.
• Deliver and support production-ready data solutions through version control, testing, deployment, performance monitoring, and clear documentation.
• Collaborate across DT&D and the business. Work with Product Managers, TDA Analysts, Ag & Trading business experts, Data Scientists, Engineers, and Governance Specialists to deliver scalable data solutions and drive meaningful business outcomes.
• Build a strong understanding of Trading and Supply Chain workflows, challenge assumptions constructively, and take ownership of data products that drive business impact across functions.

Qualifications

• Have a Bachelor's degree with minimum 9 years of professional work experience.
• Demonstrated hands-on experience partnering with data engineers to build data products. 
• Strong SQL skills, including joins, aggregation, window functions, data validation and query troubleshooting. 
• Practical Python skills for data exploration, transformation, automation and integration. 
• Experience with a cloud data warehouse or lakehouse, and an understanding of relational and analytical data modelling. 
• Ability to profile unfamiliar data, investigate anomalies and explain how data definitions affect an analysis. 
• Experience with Git, code review, automated testing and repeatable delivery practices. 
• Ability to work directly with stakeholders, ask precise questions and communicate technical findings clearly. 
• Ability to use GenAI tools effectively and responsibly to improve productivity in data engineering, analysis and documentation, while validating outputs, protecting sensitive information and adapting working practices as AI capabilities evolve. 
• Evidence of taking a problem from discovery to a working, validated solution, while identifying dependencies and seeking specialist help when needed. 
• Curiosity about commercial processes and a willingness to build domain knowledge through practical work.