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