Senior IT Architect (Enterprise Applications / Clinical Platforms) at mckesson – USA | Jobyssey

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.

What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.

Role Summary

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 an 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.

Technical Environment

Cloud-Native Integration: API-led connectivity, event-driven architecture, pub/sub, schema management, idempotency, retries, backpressure
APIs: RESTful services, OpenAPI/Swagger, API gateways, versioning and contract-first design
Messaging: Kafka (or equivalent), queues/streams, event schema design
Platform Patterns: Microservices, containerization (Docker), service discovery, configuration management; service mesh (where applicable)
Data & Performance: Oracle and relational databases; Redis (caching); Elasticsearch; analytics/pipelines (e.g., Databricks)
Interoperability (nice to have): HL7v2, FHIR (R4+), SMART on FHIR, OAuth2/OIDC, C-CDA
Observability: Logging, metrics, distributed tracing (e.g., Dynatrace)
Cloud & Platforms: Azure, AWS, GCP, Cloud Foundry
Engineering Practices: CI/CD, automated testing, code reviews, architecture documentation

Key Responsibilities

Architecture Leadership & Strategy

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.

Cloud-Native Integration Architecture

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-default integration approaches (OAuth2/OIDC, least privilege, secrets management, threat modeling).

Healthcare Interoperability (Optional) & Partner Integrations

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.

Operational Excellence & Production Ownership

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.

AI Enablement (Preferred)

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.

Minimum Requirement

Degree or equivalent and typically requires 7+ years of relevant experience.

Education

Bachelor’s degree in computer science, Software Engineering, Information Systems, or equivalent practical experience.

Critical Skills

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 have: familiarity with HL7/FHIR and/or willingness to learn quickly as needed for partner integrations.

Preferred Skills

Optional/nice to have: 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).

What Success Looks Like

At 6 Months

You have established and socialized a target-state cloud-native integration architecture, with clear standards for APIs, events, resiliency, and security.

You have delivered at least one end-to-end initiative (design through production) for a key integration capability (e.g., event streaming, API modernization, partner onboarding accelerator).

Teams are adopting your reference patterns and documentation with reduced integration friction and faster delivery.

You are actively supporting production operations through improved observability, incident learnings, and reliability improvements.

At 12 Months

You own and continuously improve the integration architecture across multiple products and partner pathways, with measurable gains in reliability, scalability, and onboarding speed.

You have matured governance for integration assets (API standards, event schemas, implementation guides where applicable, and conformance testing) and created a repeatable delivery playbook across the SDLC.

You are a trusted technical leader influencing roadmaps, investment decisions, and cross-team alignment.

You have helped teams incorporate intelligent capabilities responsibly (where applicable) through clear evaluation, monitoring, and risk controls.

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here.

Our Base Pay Range for this position

$133,100 - $221,900

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