SURVEILLANCE AND INTEROPERABILITY DATA ENGINEERING

Arhs

Full-timesenior

Job description

1. Interoperability Middleware Design<ul><li>Design and develop technical specifications for an interoperability middleware based on client's SMART Guidelines.</li><li>Support subject matter experts in defining and validating data dictionary mappings.</li><li>Design mapping logic between surveillance systems such as DHIS2, SORMAS, Go.Data, OpenELIS, and other health information systems.</li><li>Identify interoperability gaps and propose scalable technical solutions.</li><li>Document architecture decisions, interoperability workflows, and design trade-offs.</li><li>Develop specification frameworks aligned with client's SMART Guidelines, ICD-11, LOINC, SNOMED CT, and other healthcare interoperability standards.</li><li>Contribute to the design of AI agent frameworks and orchestration layers supporting data integration.</li></ul>2. Canonical Data Model & Data Ingestion<ul><li>Design and implement a Canonical Data Model serving as the central source of truth based on the client's Digital Adaptation Kit.</li><li>Configure and optimize relational and graph/network database environments.</li><li>Develop scalable ingestion frameworks capable of operating in both cloud and on-premises environments.</li><li>Implement staging layers for data ingestion, validation, transformation, harmonization, and quality assurance.</li><li>Design synchronization mechanisms supporting low-resource environments and offline data collection.</li></ul>3. Automated Data Pipelines<ul><li>Develop production-grade ETL/ELT pipelines to automate ingestion and processing of surveillance datasets.</li><li>Build AI-assisted workflows and agent-driven mechanisms for extracting and integrating data from systems such as DHIS2, SORMAS, EWARS, and other external sources.</li><li>Implement automated processes for data validation, cleansing, deduplication, normalization, and harmonization.</li><li>Ensure pipelines efficiently process heterogeneous datasets while delivering high-quality data for analytics and modelling teams.</li><li>Optimize pipeline performance, scalability, and reliability.</li></ul>4. Reporting Infrastructure & Data Services<ul><li>Develop automated workflows for weekly and monthly surveillance reports and situation reports (SitReps).</li><li>Build APIs and data export services supporting downstream analytics and modelling.</li><li>Develop clean analytical datasets optimized for threshold analysis and collaborative modelling.</li><li>Support dashboard development and data visualization initiatives.</li><li>Ensure reporting infrastructure meets security, performance, and interoperability requirements.</li></ul> <ul><li>Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, or a related technical field is required,</li><li>At least 8 years of relevant experience across software architecture and data engineering.</li><li>At least 4 years specifically in public health information systems.</li><li>Expert knowledge in spoken and written English.</li><li>Intermediate knowledge of French and any other UN language would be an asset, but not mandatory.</li><li>Experience managing cloud-based and on-premise data platforms</li><li>Proven ability to design and implement interoperability middleware and connectors (e.g., DHIS2, SORMAS, OpenMRS)</li><li>Experience harmonizing surveillance data for advanced analytics and epidemiological modelling</li><li>Familiarity with client's SMART Guidelines and Digital Adaptation Kits (DAKs)</li><li>Demonstrated experience designing canonical data models and interoperability architectures</li><li>Experience supporting deployment and scaling of interoperable digital health solutions at country level</li><li>Experience leading engineering teams and documenting architectural decisions</li><li>Strong track record of collaborating with epidemiologists, surveillance officers, emergency response teams, and cross-functional stakeholders to translate operational needs into reliable, scalable systems</li></ul>