Data Quality Analyst

• Monitor completeness, consistency and plausibility of data across sources and identify issues independently
• Design and implement data quality checks and recommend additional controls across pipelines and source systems
• Develop tooling and automated monitoring with the Analytics Engineer
• Reconcile figures across related sources and investigate discrepancies
• Contribute to data quality reporting, rules and standards
• Investigate data issues and trace root causes across source systems, pipelines and data models
• Work with the Analytics Engineer, MEAL and country teams to resolve issues at source and confirm resolution
• Handle assigned data quality and data export requests
• Document findings and hand over issues requiring deeper analysis, reporting changes or engineering work
• Support the data quality incident process with evidence for decisions
• Write concise summaries covering issue cause, impact and recommended action
• Report data quality status to the Analytics Team and source-data owners
• Use charts and tables to show issue scope and impact
• State data limitations clearly
• Maintain reproducible checks and analyses using version-controlled code and notebooks
• Follow and improve standards for code, documentation and data use
• Use shared data models and agreed metric definitions
• Seek methodological review and provide constructive review of colleagues’ work
• Build knowledge of Imagine’s data, programmes and operating contexts
• Handle sensitive data in line with data governance and privacy policies
• Report to the Director of Analytics and work closely with the Data Scientist

• Bachelor’s degree in Statistics, Mathematics, Economics, Data Science, Computer Science or a related quantitative field, or equivalent practical experience
• At least 3 years in a data analyst or closely related quantitative role
• Experience monitoring, validating or reconciling data and tracing issues to their source
• Experience working with imperfect real-world data and explaining its impact on results
• Experience building automated checks or reports that others rely on
• Experience communicating findings in writing to non-technical readers
• Experience working with remote teams across multiple countries and time zones
• Strong SQL, including joins, aggregations and window functions across large tables
• Proficiency in at least one key programming language, such as Python, for automation, data analysis and reproducible research
• Data profiling and validation techniques, including completeness, consistency and plausibility checks
• Applied statistics for identifying anomalies, including distributions, outliers and trends
• Clear data visualisation
• Reliable internet access
• Alignment with core working hours across time zones
• Ability to work independently in a virtual environment and coordinate with colleagues across global teams
• Experience in the non-profit, international development or social sector is advantageous but not required
• Experience querying analytical or columnar databases, data quality testing frameworks, reproducible workflows using notebooks and Git, data pipelines, dbt-modelled data warehouses, field data tools, BI tools or spreadsheet tools is preferred or advantageous

• Full-time remote work
• Opportunity for occasional international travel for in-person team meetings, field support or training
• Equal opportunity employer