Key Responsibilities: Design/maintain extraction transform load (ETL) workflows, dictionaries, and validation rules; run routine DQ checks and produce facility DQ scorecards. Coordinate encrypted transfers, access controls, and audit logs; support in country teams to execute central scripts locally. Build analysis ready datasets and documentation (README, data schemas, provenance). Manage the full data engineering pipeline across nine to ten countries, covering extraction, transformation, validation, documentation, and version control all study datasets. Implement and maintain data quality frameworks, including completeness, internal consistency, timeliness, audit trails, reconciliation checks, and facility feedback on data gaps. Maintain secure country specific servers, encryption, anonymization processes, access control registries, and audit logs for compliance with national data laws. Ensure correct execution of script based federated analysis workflows, supporting in country teams to run centrally developed code without exporting raw data. Produce quarterly clean datasets, documentation packs, and harmonized structures ready for analysis (including wide/long reshaping, panel construction, facility period indexing). Collaborate with analysts and statisticians to support data linkage, model preparation datasets, and metadata curation. Masters in Biostatistics/Health Informatics/Data Management. Mandatory At least 3 years working experience managing large routine health datasets and health facility assessments. Mandatory SQL/Python/R; data modelling; data quality frameworks; documentation; security best practice. Mandatory