Data Quality Analyst

About Imagine Worldwide

All children have immense potential, but hundreds of millions don’t have access to the learning they need. Imagine Worldwide believes that every child should be empowered with the basic right to literacy and numeracy. We provide an innovative education technology solution and implementation model to the global literacy/numeracy learning crisis using the onebillion application and various toolkits and systems to support implementation.

Our model is massively scalable, evidence-based, and can deliver foundational learning to millions of the most marginalized children.

Imagine Worldwide partners with governments, organizations, and communities to provide child-directed, tablet-based learning that is accessible, effective, and affordable. We are incorporated in California (United States) as a nonprofit organization, but operate in Sub-Saharan Africa through locally-registered entities in Malawi, Sierra Leone, and Tanzania. Learn more on our website.

Role Overview

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 the checks that catch problems early, and investigates issues raised across the organisation. Where an issue requires deeper analysis, reporting changes or engineering work, the role provides its findings and works with the relevant team member to resolve it.

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
• 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
• 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.

Requirements

• The qualifications below are grouped by importance. Core requirements are needed to do the role from the start. Strongly preferred skills shorten the time it takes to become effective with Imagine’s data. Advantageous experience adds value but is not expected.
• Education
• A bachelor’s degree in Statistics, Mathematics, Economics, Data Science, Computer Science or a related quantitative field, or equivalent practical experience.
• Experience
• At least 3 years in a data analyst or closely related quantitative role.
• Experience monitoring, validating or reconciling data, and tracing issues back to their source.
• Experience working with imperfect real-world data, such as missing records, inconsistent sources or late-arriving data, and explaining how those issues affect a result.
• 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.
• Experience in the non-profit, international development or social sector is an advantage but not a requirement.
• Technical Skills: Core
• 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.
• Technical Skills: Strongly Preferred
• Querying analytical or columnar databases such as ClickHouse, BigQuery, Snowflake or Redshift.
• Data quality testing frameworks such as dbt tests, Great Expectations or Soda.
• Reproducible workflows using notebooks and Git.
• Technical Skills: Advantageous
• An understanding of data pipelines and how a dbt-modelled data warehouse is structured.
• Education, programme monitoring or field survey data.
• Field data collection tools such as CommCare, ODK or KoboToolbox.
• BI tools such as Apache Superset, Metabase or Power BI.
• Spreadsheet tools for sharing data with stakeholders.