kpler - Junior Data Quality Analyst
Requirements
• 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. • 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. • We are a dynamic company dedicated to nurturing connections and innovating solutions to tackle market challenges head-on. If you thrive on customer satisfaction and turning ideas into reality, then you’ve found your ideal destination. Are you ready to embark on this exciting journey with us? • We make things happen • We act decisively and with purpose, going the extra mile. • We build together • We foster relationships and develop creative solutions to address market challenges. • We are here to help • We are accessible and supportive to colleagues and clients with a friendly approach. • Our People Pledge • Don’t meet every single requirement? Research shows that women and people of color are less likely than others to apply if they feel like they don’t match 100% of the job requirements. Don’t let the confidence gap stand in your way, we’d love to hear from you! We understand that experience comes in many different forms and are dedicated to adding new perspectives to the team.
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
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