Data Quality Analyst

The role

The Data Quality Analyst monitors data across the organization's ecosystem, including device, program, and field data. This person investigates data issues, builds automated checks, and communicates findings to resolve discrepancies.

What you will do

• Monitor completeness, consistency and plausibility of data across sources
• Design and implement data quality checks and recommend further controls
• Develop tooling and automated monitoring
• Reconcile figures across related sources and investigate discrepancies
• Investigate data issues and trace them to root causes
• Work with Analytics Engineer, MEAL and country teams to resolve issues
• Handle assigned data quality and data export requests
• Write concise summaries of data issues and investigations
• Report on the state of data quality to the Analytics Team and source data owners
• Keep checks and analyses reproducible using version-controlled code and notebooks

Required qualifications

• 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 back to their source
• Experience working with imperfect real-world data
• 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)
• Data profiling and validation techniques
• Applied statistics for identifying anomalies
• Clear data visualisation

Nice to have

• Experience in the non-profit, international development or social sector
• 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
• 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

About the company

Imagine Worldwide is a nonprofit organization that provides an innovative education technology solution to address the global literacy and numeracy learning crisis. They partner with governments, organizations, and communities to provide child-directed, tablet-based learning.