Data Quality Analyst

The Data Quality Analyst focuses on data quality across Imagine’s data ecosystem. The role proactively monitors data from devices, programme systems, and field sources, builds and improves checks to catch problems early, and investigates issues raised across the organization. Imagine’s data covers learning activity from tablets in more than 1,500 schools, along with programme, operational, and field data. Devices sync intermittently, and sources don’t always agree, so checking data against what is happening in schools and in the field is central to the work.

The role reports to the Director of Analytics and works closely with the Data Scientist, who provides methodological support. It suits an analyst who enjoys tracing a problem back to its source and wants to keep developing in a small team. As they build context on Imagine’s data, the role takes on more ownership of the team’s data quality reporting and standards.

Key Responsibilities

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

• 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

• 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

• 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.