Data Engineer – Surveillance and Interoperability

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ARHS Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2003

🏢 Enterprise

☁️ SaaS

🤖 Artificial Intelligence

Enterprise • SaaS • Artificial Intelligence

ARHS Group is a company specializing in providing advanced and reliable information systems. They offer a comprehensive range of IT solutions and services, including software development, data science, infrastructure scaling, digital trust, mobile development, machine learning, and cloud integration. Their solutions are tailored to meet specific project requirements, ensuring delivery on time, within budget, and within scope. With offices in multiple European cities, ARHS Group positions itself as a key player in IT project execution and digital transformation services.

📋 Description

• 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. • 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.

🎯 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.

🏖️ Benefits

• Employees can work remotely