Surveillance and Interoperability Data Engineering

1. 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.

2. 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.

3. 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.

4. 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