Key Responsibilities
- Monitor and safeguard data integrity: Track data quality KPIs across multiple systems to detect inconsistencies, anomalies, and gaps, escalating critical issues to protect global commodity data reliability.
- Build intuitive visual analytics: Design, deploy, and maintain dynamic BI dashboards and reports to present clear data health insights to key stakeholders.
- Perform exploratory analysis: Utilize SQL and BI software to investigate datasets, discover underlying patterns, and resolve complex data quality challenges.
- Drive process automation & AI integration: Streamline data validation workflows by leveraging automation tools, prompt engineering, and modern AI techniques.
- Collaborate cross-functionally: Partner closely with Data Operations, Business Unit Engineers, Data Engineers, and BI teams to align data quality standards with business needs.
- Conduct root-cause investigations: Address ad-hoc stakeholder inquiries and deliver swift, accurate data analysis to support operational decision-making
Experience & Background
- 1–2 years of hands-on experience in a data-focused role or internship (e.g., Data Analysis, BI, or Data Science).
- Bachelor’s degree (or higher) in a quantitative field such as Economics, Computer Science, Statistics, Business, or equivalent practical experience.
- Solid foundational SQL skills with the ability to query, filter, aggregate, and join complex tables efficiently.
- Practical familiarity with Python, GitHub, and spreadsheet tools (Excel or Google Sheets) for data manipulation and profiling.
- Working experience with modern BI visualisation tools such as Looker, Tableau, or Power BI.
- Meticulous attention to detail, strong problem-solving skills, and a desire to take end-to-end ownership of data accuracy.
Nice-to-haves:
- Exposure to or strong interest in energy, commodities, or Oil & Gas markets.
- Active interest in AI tools and best practices, with a focus on improving documentation, streamlining workflows, and enhancing quality assurance processes.