Surveillance and Interoperability Data Engineering

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.

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.

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.

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