Staff Interoperability Engineer

Staff Interoperability Engineer (healthcare solution)

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Location: Remote from Spain (an indefinite Spanish employment contract)

Intellias is looking for a Staff Interoperability Engineer to join a large-scale digital healthcare initiative focused on designing, building, and scaling healthcare interoperability solutions that enable secure, standards-compliant health data exchange across the U.S. healthcare ecosystem.

This is a Staff-level Individual Contributor role for an experienced engineer who combines deep expertise in healthcare interoperability, distributed backend systems, API architecture, and cloud-native engineering. You will define technical direction, solve the platform's most complex interoperability challenges, and establish engineering standards that enable teams to build reliable, scalable, and compliant healthcare integrations.

The role combines deep hands-on engineering with broad technical influence across interoperability architecture, FHIR implementation, API security, healthcare standards, and AI-enabled engineering practices.

Project Overview:

The project is building a FHIR-based healthcare data platform that enables secure interoperability across EHR systems, healthcare providers, payers, national healthcare exchange networks, laboratories, pharmacies, wearable devices, and digital health applications.

The platform processes healthcare data at national scale, supporting Patient Access APIs, Bulk FHIR APIs, Provider Directory APIs, SMART on FHIR, and real-time healthcare integration services while maintaining compliance with U.S. healthcare interoperability standards and regulations.

The engineering environment combines Python, Apache Spark, Kafka, Kubernetes, cloud-native infrastructure, FHIR R4, HL7, REST APIs, OAuth2/OIDC, SMART on FHIR, distributed event-driven architectures, and AI-assisted engineering.

This is an AI-native engineering environment, where coding agents and reusable AI capabilities are used to accelerate implementation, testing, validation, documentation, and interoperability engineering without compromising correctness, security, or PHI safety.

Requirements:

• 8+ years of professional experience in healthcare interoperability, Health IT, backend engineering, or distributed integration platforms.
• Proven Staff-level technical ownership of complex interoperability platforms and production healthcare integration solutions.
• 4+ years of hands-on experience implementing FHIR-based solutions, including production FHIR R4 implementations.
• Extensive experience integrating multiple EHR/EMR platforms, including systems such as Epic, Oracle Health (Cerner), Allscripts, athenahealth, eClinicalWorks, Meditech, NextGen, or comparable platforms.
• Proven experience implementing Patient Access APIs, Provider Directory APIs, Bulk FHIR APIs, and other FHIR-based interoperability services.
• Production experience integrating with HIE/HIN ecosystems, including Carequality, CommonWell, eHealth Exchange, TEFCA, or comparable national interoperability frameworks.
• Expert knowledge of FHIR R4, including resources, profiles, extensions, search parameters, Bundles, validation, and implementation patterns.
• Deep understanding of US Core Implementation Guides, USCDI, and U.S. healthcare interoperability requirements.
• Strong knowledge of SMART on FHIR, OAuth 2.0, OpenID Connect, SMART scopes, backend services, and healthcare authorization models.
• Strong understanding of HL7 v2.x, C-CDA, and healthcare data transformation patterns.
• Experience with healthcare terminology standards including SNOMED CT, LOINC, RxNorm, ICD-10, CPT, and CVX.
• Strong hands-on programming skills in Python, plus experience with at least one additional language such as Java, TypeScript/JavaScript, Go, or C#.
• Experience designing and building RESTful APIs, FHIR APIs, event-driven architectures, and distributed backend services.
• Strong knowledge of API security, including OAuth 2.0, JWT, mTLS, rate limiting, audit logging, and consent management.
• Experience with messaging technologies such as Kafka, RabbitMQ, or comparable event-driven systems.
• Experience building distributed systems using Spark, Kubernetes, Docker, and cloud-native infrastructure.
• Strong knowledge of ETL/ELT, CDC, streaming, and asynchronous processing patterns.
• Proven experience establishing reusable interoperability frameworks, engineering standards, implementation guides, and technical documentation.
• Practical experience using AI coding agents such as Claude Code or comparable AI-assisted engineering tools.
• Strong engineering judgment regarding deterministic processing versus AI-assisted implementation.
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