Background

Wellcome Research Programme

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.

Qualifications

• 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

• 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.

• SQL
• Python
• R
• Data modelling
• Data quality frameworks
• Documentation
• Security best practice

• 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