Senior IT Architect (Enterprise Applications / Clinical Platforms)

McKesson•Irving, TX

About The Position

Ontada is an oncology data science and technology business dedicated to transforming the fight against cancer. We specialize in real-world data and evidence generation that accelerates life science research, clinical technologies that support community providers with precise care, and provider engagement channels that enable education and insights. We are hiring a n IT Architect to define and drive cloud-native integration architecture across Ontada products and partner ecosystems. This role owns solution and platform architecture for API-led connectivity, event-driven integration, and secure data exchange—ensuring our systems are scalable, reliable, observable, and straightforward to evolve. Familiarity with healthcare interoperability standards (HL7/FHIR, including SMART on FHIR) is a plus for certain partner pathways, but the primary focus is strong enterprise-architecture fundamentals applied to modern cloud-native integration models. Success in this role is measured by the clarity and adoption of your reference architectures and standards, the reliability and scalability of the integration ecosystem you enable, and your ability to influence decisions across multiple initiatives while mentoring and elevating engineering teams. Familiarity with AI/ML and NLP capabilities used in products (e.g., LLM-enabled workflows, retrieval patterns, evaluation, and governance) is a plus as Ontada expands intelligent experiences, but is not the core requirement.

Requirements

Degree or equivalent and typically requires 7+ years of relevant experience.
Bachelor’s degree in computer science , Software Engineering, Information Systems, or equivalent practical experience.
7+ years of software engineering experience, including 4+ years in architecture (solution, platform, or enterprise) with ownership across multiple systems.
Strong enterprise integration architecture skills across REST APIs, event-driven messaging, and secure data exchange patterns.
Strong technical fundamentals in cloud-native architecture (microservices, containers, resiliency patterns, observability) on Azure, AWS, or GCP.
Experience designing and operating large-scale, multi-tenant services with reliability, scalability, and security best practices.
Ability to drive cross-functional technical decisions, mentor engineers/architects, and communicate clearly through documentation and design reviews.
Strong communication and stakeholder-management skills, with a track record of influencing cross-team decisions through documentation and design reviews.

Nice To Haves

Familiarity with healthcare interoperability standards (HL7/FHIR, including SMART on FHIR)
Familiarity with AI/ML and NLP capabilities used in products (e.g., LLM-enabled workflows, retrieval patterns, evaluation, and governance)
Hands-on experience with FHIR (profiles, implementation guides, SMART on FHIR) and/or HL7v2, including conformance and integration testing.
Experience integrating with multiple EMRs and partner ecosystems; familiarity with common healthcare workflows and data exchange constraints.
Hands-on experience with microservices frameworks (e.g., Spring or equivalent) and API management practices.
Experience with messaging and integration platforms (Kafka, service bus/queues, stream processing) and caching technologies (e.g., Redis).
Experience with platform engineering patterns such as service mesh, policy-as-code, and automated architecture validation in CI/CD.
Experience with observability platforms (e.g., Dynatrace) and production operations best practices.
AI/ML product delivery experience (LLM-enabled workflows, evaluation, and responsible AI governance).
Experience with Elasticsearch and/or analytics platforms (e.g., Databricks).

Responsibilities

Drive architecture for interoperability and integration capabilities across systems, services, and product offerings.
Partner with Product Owners, Sales, Business Analysts, Quality Assurance, and Project Management to translate functional and non-functional requirements into target architectures and delivery plans.
Define architecture standards, reference implementations, and guardrails for APIs, integration patterns, security, and operational readiness.
Evaluate solution options and drive alignment across stakeholders; communicate tradeoffs, risks, and recommendations to technical and non-technical audiences.
Provide technical guidance to coach, motivate, and develop engineers and architects.
Define and govern integration reference architectures across APIs, events, and asynchronous workflows (request/response, pub/sub, choreography vs. orchestration).
Establish platform patterns and guardrails for API gateways, service-to-service communication, schema/version management, and contract testing.
Design for resiliency and performance using proven patterns (timeouts, retries, circuit breakers, bulkheads, idempotency, caching) and align to SLOs/SLAs.
Guide event -driven integration (e.g., Kafka or equivalent) with well-defined event schemas, lifecycle management, and operational controls.
Collaborate with security and platform teams to ensure secure-by-design integration approaches (OAuth2/OIDC, least privilege, secrets management, threat modeling).
When applicable, drive design for healthcare data exchange leveraging HL7v2 and/or FHIR (R4+), including SMART on FHIR, to enable integrations with EMRs and partner systems.
Partner with domain SMEs and external stakeholders to define interoperability expectations (security, conformance, and onboarding) and ensure integrations are scalable and supportable.
Define API contracts, versioning strategy, and error taxonomy to enable predictable integrations for internal and external consumers.
Partner with engineering teams to implement and evolve reusable integration components, adapters, and accelerators that reduce time-to-integrate.
Collaborate with engineering in release, sprint, and project planning to ensure delivery commitments are tracked and met across the SDLC.
Demonstrate strong architectural ownership in production operations to address customer needs for support, issue analysis, enhancements, and corrective actions.
Evolve software systems, infrastructure, and architecture to support growth, SLAs, availability, and continued operation.
Drive operational readiness practices including observability, runbooks, incident response, and post-incident improvements.
Resolve technical problems, issues, and escalations across delivery teams and project activities.
Partner with product and engineering teams to identify and deliver AI-enabled capabilities, from discovery and design through deployment, monitoring, and continuous improvement.
Guide architecture patterns for integrating model services into cloud-native systems (APIs, events, data pipelines) with appropriate security , privacy, and compliance controls.
Promote MLOps / LLMOps practices (versioning, CI/CD, automated testing, observability, rollback) and responsible AI governance.
Collaborate on evaluation approaches to ensure AI features are reliable and safe for production use.