Imagine Worldwide - Data Quality Analyst

• The Data Analyst’s responsibilities fall into four pillars: data quality monitoring and improvement, data issue investigation, communicating findings, and analytical practice. • Data Quality Monitoring and Improvement • The role takes a proactive approach to data quality, identifying risks and gaps before they affect reporting or decision-making. • Monitor the completeness, consistency and plausibility of data across Imagine’s sources, and identify issues independently. • Design and implement data quality checks, and recommend where further checks or controls are needed across pipelines and source systems. • Develop tooling and automated monitoring that make data quality issues visible, working with the Analytics Engineer on implementation within the platform. • Reconcile figures across related sources and investigate discrepancies. • Contribute to the team’s data quality reporting, rules and standards. • Data Issue Investigation • The role is the Analytics Team’s first point of investigation when data issues are identified, whether through monitoring or raised by colleagues. • Investigate data issues and trace them to their root cause across source systems, pipelines and data models. • Work with the Analytics Engineer, MEAL and country teams to resolve issues at source and confirm their resolution. • Handle assigned data quality and data export requests from the Analytics Team’s request process. • Document findings and hand over issues that require deeper analysis, reporting changes or engineering work to the relevant team member. • Support the team’s data quality incident process by providing the evidence needed for decisions. • Communicating Findings • Write concise summaries of data issues and investigations, covering the cause, the impact and the recommended action. • Report on the state of data quality to the Analytics Team and, where appropriate, to the teams that own the source data. • Use clear charts and tables to show the scope and impact of issues. • State data limitations plainly, so that figures are not over-interpreted when shared further. • Analytical Practice • Analytical Practice • Analysis is only useful to a small team if others can check it, reuse it and build on it. • Keep checks and analyses reproducible, using version-controlled code and notebooks that someone else could rerun and arrive at the same result. • Follow the Analytics Team’s standards for code, documentation and data use, and contribute to improving them as the team grows. • Use the team’s shared data models and agreed metric definitions as the starting point for analysis, and flag where they fall short rather than working around them quietly. • Ask for review of methods and conclusions from the Data Scientist and Director of Analytics, and give constructive review on colleagues’ work in return. • Build a working knowledge of Imagine’s data, programmes and operating contexts, so that analyses reflect how the programme actually runs on the ground. • Handle sensitive data responsibly, in line with Imagine’s data governance and privacy policies.