Principal Solution Architect Jobs in Pennsylvania

Critical to success is deep familiarity with clinical operations and clinical trial management domains, including understanding of study design, site and patient management, regulatory compliance workflows, data integration across clinical and operational systems, and how technology and AI/agent-based capabilities can be applied to optimize trial execution and Clinical Operations efficiency.  

Preference will be given to candidates with hands-on experience in clinical trial management, site operations, patient cohort management, or related clinical domains, combined with ability to use enterprise architecture tools and capability models as the stable backbone for processes, products, data, and automation across business units. 

Job overview 

The Principal Enterprise Architect is accountable for co-designing target-state enterprise architectures and reference models with business, technology, and product leaders, and for prescribing standards, patterns, and guardrails that ensure consistency and scalability across IQVIA. The role provides a portfolio-wide view of business and technical capabilities, surfacing cross-cutting needs and opportunities to consolidate platforms and services in both application and data domains. Within clinical operations and clinical trial management, the architect will establish patterns and standards that govern how data flows across trial planning, site activation, patient recruitment, data collection, and trial closeout workflows, ensuring that technology investments support operational excellence and regulatory compliance. 

Key responsibilities 

• Co-design and maintain enterprise architecture principles, standards, and reference architectures in partnership with business, technology, and product leaders, with explicit focus on clinical operations and clinical trial management to ensure that architecture patterns reflect the realities of study execution, site management, regulatory compliance, and patient safety. 

• Define target enterprise data, AI, and interoperability reference architectures, including standard patterns for event driven architectures (e.g., Kafka), API management (e.g., Apigee, Azure API Management, Kong), and AI/ML platforms (e.g., Azure ML, SageMaker), and ensure initiatives align to these standards and roadmaps. Within clinical domains, establish patterns for EDC integration, clinical data warehousing, site and patient data orchestration, and agentic AI for trial feasibility, site matching, and cohort discovery. 

• Partner with business units to translate strategies and OKRs into capability- and outcome-based architecture roadmaps that must with fully rationalized financials and an applied knowledge of, rather than project-specific solutions. In clinical operations, this includes collaborating with trial operations, site engagement, patient recruitment, and data management leaders to build roadmaps that optimize study start-up timelines, site activation efficiency, patient enrollment velocity, and data quality. 

• Provide an enterprise-wide view of interrelated business and technical capabilities, identifying patterns, shared needs, and reusable platforms and services. In Clinical Operations contexts, surface opportunities to consolidate disparate site management tools, EDC platforms, and patient data systems, and identify where agentic AI and automation can reduce manual operational burden. 

• Guide senior stakeholders through architectural options, trade-offs, and investment implications using clear, business-focused narratives and models. For clinical initiatives, articulate how architecture decisions impact trial timelines, regulatory risk, site partner experience, and patient enrollment. 

• Ability to translate clinical domain objectives (study, regulatory requirements, and site/patient operational constraints) into reusable capability models and architectural blueprints that enable consistent, scalable trial delivery across geographies and therapeutic areas. 

• Use enterprise architecture tools (e.g., LeanIX, Ardoq, MEGA) to define and maintain stable capability models and EA meta-models; map capabilities to processes, systems, products, outcomes, and key data domains; and support intake of new ideas into the enterprise roadmap through formalized architecture governance and portfolio intake processes and manage financial health across the portfolio In clinical domains, use capability models to organize trial operations (study design, site management, patient cohort management, data collection, compliance, closeout) and map them to technology investments and data flows. 

• Govern and rationalize the EA application and tooling portfolio, including inventories of business, data, and technology assets, and promote consistent adoption of standards across the organization. In clinical domains, rationalize point solutions and duplicate site/patient management tools; establish governance for new clinical technology investments to ensure alignment with reference architectures. 

• Collaborate with other business leaders, product managers, and domain architects and technology leaders to identify and mature shared services, platforms, and common patterns. Specifically, partners with clinical domain architects and product teams to evolve and standardize architectures for site engagement, patient recruitment, trial data management, and regulatory compliance automation. 

• Monitor technology and industry trends (especially AI/ML, data platforms, lake house governance patterns, life sciences tech, clinical trial innovation, and agentic AI applications in clinical workflows) and incorporate relevant innovations into enterprise standards and roadmaps. Track emerging capabilities in decentralized trials, wearable data integration, AI-driven patient matching, and agent-based workflow automation to keep IQVIA's Clinical Operations architecture competitive. 

• Mentor and influence architects and senior engineers to raise architecture maturity and adherence to enterprise standards across delivery teams. Develop coaching and reference architectures that enable clinical product and platform teams to build scalable, interoperable solutions aligned to enterprise standards. 

Skills and Experience 

• 15+ years in software development with 10–12+ years in enterprise architecture in large, complex organizations. 

• Demonstrated experience owning enterprise-wide technology strategies and roadmaps, including business architecture, value streams, capability maps, and customer journeys and apply financial acumen to ensure health of the portfolio 

• Recent, relevant enterprise experience with applied data science, AI/ML, and agentic AI solutions, including designing architectures for LLM-based and agent-based systems using technologies such as vector databases (e.g., Pinecone, Redis), orchestration frameworks (e.g., Lang Chain), and MLOps platforms (e.g., MLflow, Azure ML). Specific to clinical domains: hands-on experience with AI/ML applications in patient cohort discovery, site feasibility modeling, protocol complexity assessment, or operational forecasting for trial success. 

• Strong enterprise data architecture background, including implementation of data lakehouse architectures on platforms such as Databricks and Snowflake using medallion layering, plus data governance platforms and automation, data catalogs, ontologies, and taxonomies. In clinical contexts: demonstrated experience architecting clinical data warehouses, data warehouses, data integration, harmonized patient and site data models, and regulatory-compliant data governance for clinical trial data. 

• Deep, hands-on familiarity with clinical operations and clinical trial management, including study design, regulatory pathways, site activation and management, patient recruitment and cohort identification, workflows, protocol adherence monitoring, safety and pharmacovigilance reporting, and trial closeout and data archival. Preference for candidates with direct exposure to site partner needs, patient engagement challenges, and operational constraints that shape how technology can optimize trial delivery. 

• Experience in healthcare or life sciences, with a strong preference for working closely with clinical trial management, clinical operations, or related domains. Candidates should have directly supported Clinical Operations functions (site operations, patient recruitment, data management, regulatory compliance) and understand how technology architecture decisions cascade into operational efficiency, regulatory risk, and trial outcomes. 

• Deep understanding of modern architecture styles (event-driven, microservices-based, RESTful and alternative integration patterns, API-first) and how to codify them as enterprise standards and reference architectures. For clinical domains: ability to apply these patterns to site management workflows, patient data orchestration, and real-time trial monitoring dashboards. 

• Experience with both on-premises and cloud architecture on Azure and/or AWS. 

• Advanced proficiency with enterprise architecture tools (e.g., LeanIX, Ardoq, MEGA, or equivalent) and expertise in EA meta-models, capability modeling, portfolio management, data and application inventories, and architecture governance; demonstrated ability to design and operationalize EA meta-models that organize business capabilities, technology assets, data domains, and integration patterns at enterprise scale. For clinical applications: proven skill in building capability models that reflect trial operations, site partner experience, and patient journeys, enabling consistent translation of Clinical Operations strategy into technology roadmaps. 

• Soft skills should include strong executive presence, articulate communication and influence skills, collaboration, and tenacity with a track record of partnering with senior business, technology, and product leaders to drive adoption of standard-driven enterprise architectures at scale. Specifically, ability to engage clinical operations executives, site partners, patient advocates, and regulatory stakeholders to build consensus on architecture directions and demonstrate how technology investments drive operational and patient outcomes.