{"id":3157685,"url":"https://alion.io/job/imagine-worldwide-data-quality-analyst","title":"Data Quality Analyst","company":{"id":679437,"name":"Imagine Worldwide","domain":"imagineworldwide.com","url":"https://alion.io/company/imagineworldwide","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":["EG","KE","NG","ZA","DZ","AO","BJ","BW","BF","BI","CM","CV","TD","CF","KM","CD","DJ","GQ","ER","SZ","ET","GA","GM","GH","GN","GW","CI","LS","LR","MG","MW","ML","MR","MU","YT","MA","MZ","NA","NE","CG"],"hiring_countries_total":56,"salary":null,"salary_estimate":{"min_usd":62000,"max_usd":125000,"period":"year","method":"global_role_seniority_cell","sample_n":1854},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":true},{"name":"ClickHouse","optional":true},{"name":"dbt","optional":true},{"name":"Git","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Great Expectations","optional":true},{"name":"Metabase","optional":true},{"name":"Power BI","optional":true},{"name":"Python","optional":true},{"name":"Snowflake","optional":true},{"name":"SQL","optional":true},{"name":"Superset","optional":true}],"status":"live","first_seen_at":"2026-10-23T11:05:23Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-27T17:58:51Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"About Imagine Worldwide\nAll 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.\nImagine 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.\nRole Overview\nThe 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. \nImagine’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.\nThe 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.\nKey Responsibilities\nThe Data Analyst’s responsibilities fall into four pillars: data quality monitoring and improvement, data issue investigation, communicating findings, and analytical practice.\nData Quality Monitoring and Improvement \nThe role takes a proactive approach to data quality, identifying risks and gaps before they affect reporting or decision-making. \nMonitor the completeness, consistency and plausibility of data across Imagine’s sources, and identify issues independently.\nDesign and implement data quality checks, and recommend where further checks or controls are needed across pipelines and source systems.\nDevelop tooling and automated monitoring that make data quality issues visible, working with the Analytics Engineer on implementation within the platform.\nReconcile figures across related sources and investigate discrepancies.\nContribute to the team’s data quality reporting, rules and standards.\nData Issue Investigation \nThe role is the Analytics Team’s first point of investigation when data issues are identified, whether through monitoring or raised by colleagues. \nInvestigate data issues and trace them to their root cause across source systems, pipelines and data models.\nWork with the Analytics Engineer, MEAL and country teams to resolve issues at source and confirm their resolution.\nHandle assigned data quality and data export requests from the Analytics Team’s request process.\nDocument findings and hand over issues that require deeper analysis, reporting changes or engineering work to the relevant team member.\nSupport the team’s data quality incident process by providing the evidence needed for decisions.\nCommunicating Findings\nWrite concise summaries of data issues and investigations, covering the cause, the impact and the recommended action.\nReport on the state of data quality to the Analytics Team and, where appropriate, to the teams that own the source data.\nUse clear charts and tables to show the scope and impact of issues.\nState data limitations plainly, so that figures are not over-interpreted when shared further.\nAnalytical Practice\nAnalysis is only useful to a small team if others can check it, reuse it and build on it.\nKeep checks and analyses reproducible, using version-controlled code and notebooks that someone else could rerun and arrive at the same result.\nFollow the Analytics Team’s standards for code, documentation and data use, and contribute to improving them as the team grows.\nUse 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.\nAsk for review of methods and conclusions from the Data Scientist and Director of Analytics, and give constructive review on colleagues’ work in return.\nBuild a working knowledge of Imagine’s data, programmes and operating contexts, so that analyses reflect how the programme actually runs on the ground.\nHandle sensitive data responsibly, in line with Imagine’s data governance and privacy policies.\nRequirements\nThe 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.\nEducation\nA bachelor’s degree in Statistics, Mathematics, Economics, Data Science, Computer Science or a related quantitative field, or equivalent practical experience. \nExperience\nAt least 3 years in a data analyst or closely related quantitative role.\nExperience monitoring, validating or reconciling data, and tracing issues back to their source.\nExperience working with imperfect real-world data, such as missing records, inconsistent sources or late-arriving data, and explaining how those issues affect a result.\nExperience building automated checks or reports that others rely on.\nExperience communicating findings in writing to non-technical readers.\nExperience working with remote teams across multiple countries and time zones.\nExperience in the non-profit, international development or social sector is an advantage but not a requirement.\nTechnical Skills: Core\nStrong SQL, including joins, aggregations and window functions across large tables.\nProficiency in at least one key programming language (such as Python) for automation, data analysis, and reproducible research.\nData profiling and validation techniques, including completeness, consistency and plausibility checks.\nApplied statistics for identifying anomalies, including distributions, outliers and trends.\nClear data visualisation.\nTechnical Skills: Strongly Preferred\nQuerying analytical or columnar databases such as ClickHouse, BigQuery, Snowflake or Redshift.\nData quality testing frameworks such as dbt tests, Great Expectations or Soda.\nReproducible workflows using notebooks and Git.\nTechnical Skills: Advantageous\nAn understanding of data pipelines and how a dbt-modelled data warehouse is structured.\nEducation, programme monitoring or field survey data.\nField data collection tools such as CommCare, ODK or KoboToolbox.\nBI tools such as Apache Superset, Metabase or Power BI.\nSpreadsheet tools for sharing data with stakeholders.\nCompensation & Benefits\nSalary is competitive and commensurate with experience.\nLocation\nThe Data Analyst must be based in Africa. The role will frequently collaborate and coordinate with operations teams across Africa, the US, and the UK. \nThis is a full-time remote position; the role can be based anywhere in Africa with reliable internet access and alignment with core working hours across time zones. This is a full-time remote position; the role can be based anywhere in Africa with reliable internet access and alignment with core working hours across time zones. The role may involve occasional international travel for in-person team meetings, field support or training, as needed. Candidates should be comfortable working independently in a virtual environment and coordinating with colleagues across global teams.\nApplications are reviewed on a rolling basis\n* * *\nImagine Worldwide is proud to be an equal opportunity employer. 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