Solutions Architect - CIBMTR

mcw; Milwaukee (WI); On-site; USD 140,000 - 190,000; Full time

Job summary

CIBMTR is seeking a Solutions Architect to lead design and validation of its modernized data platform. You will define end-to-end reference architectures for lakehouse, metadata, and AI consumption, and partner with vendors and IT leaders to translate strategy into secure, governable solutions.

You will mentor engineers, review vendor designs, ensure HIPAA privacy and data-use governance, and drive non-functional requirements such as scalability and resilience in a cloud-native environment.

Qualifications

• Experience designing enterprise data architectures for cloud-native platforms.
• Knowledge of HIPAA/privacy in clinical research data.
• Ability to mentor and upskill data engineers and analysts.

Responsibilities

• Define and evolve target-state reference architectures for data, analytics, and AI.
• Review designs from consultants and ensure alignment with enterprise standards.
• Collaborate with vendors and IT leaders to validate and operationalize platforms.
• Ensure scalability, security, and regulatory compliance.

Skills

Cloud architecture

Data lakehouse

AI governance

Mentorship

Tools

Delta Lake

SQL data warehouse

APIs & data integration

LLM/RAG patterns

Job description

Summary

CIBMTR is a global research registry that acquires treatment and outcomes data from transplant and cellular-therapy centers on patients who have received blood or marrow transplants, CAR-T, and gene therapies for a broad range of malignant and non-malignant indications. We curate and make data available to a worldwide network of researchers, clinicians, regulators, and other stakeholders who depend on its accuracy, completeness, and timeliness.

CIBMTR has launched an ambitious, multi-year initiative to transform and modernize its entire end-to-end data pipeline. The program includes adoption of agentic AI, migration to a lakehouse architecture, establishment of centralized metadata management, expansion of analytics capabilities, and deployment of cloud-native infrastructure.

Reporting to the Sr. IT Director, the Solutions Architect will provide senior technical leadership in documenting and validating the current-state architecture and in designing, validating, and overseeing the build-out of the future-state platform. The architect will act as a trusted technical advisor, partnering with CIBMTR vendors and IT leaders alike, translating strategic objectives into concrete, governable, and secure reference architectures spanning data, analytics, machine learning, and agentic AI. The role is equal parts design authority, technical advisor, and mentor: the architect is expected not only to shape the platform, but also to raise the capabilities of an experienced team who possess deep domain and data knowledge. Success depends on technical depth, hands-on application, sound judgment across architectural trade-offs, the ability to challenge and validate vendor designs constructively, and a genuine talent for teaching.

Primary Responsibilities

• Architecture & Solution Design: Target-state architecture. Define and evolve the end-to-end reference architecture for CIBMTR's modernized data estate, including lakehouse (e.g., medallion / Delta-style) layers, data products, semantic models, and analytics and AI consumption channels.
• Hyperscaler design & optimization. Collaborate with IT teams and vendors to design, configure, and optimize CIBMTR's cloud environment ensuring the hyperscaler architecture is right-sized for cost, performance, security, and operational simplicity. Work closely with Cloud and other technical roles to align cloud infrastructure decisions with the broader data and AI platform, driving cross-disciplinary optimization across compute, storage, networking, and managed services.
• Lakehouse migration. Lead the architectural design of the migration from the current SQL-based data warehouse to a lakehouse platform, including ingestion from treatment centers, ELT/streaming pipelines, storage and table formats, and cost and performance optimization.
• Agentic AI & ML. Establish reference architectures, patterns, and guardrails for agentic AI and machine learning - including LLM/RAG patterns, agent orchestration, tool and prompt governance, evaluation, telemetry, model monitoring, and explainability - aligned to security, privacy, and regulatory expectations.
• Metadata & governance. Architect centralized metadata management, data cataloging, lineage, and a semantic layer that turns governed data into AI-ready data products discoverable through SQL, APIs, and natural-language interfaces.
• Integration & interoperability. Design integration patterns - APIs, event-driven and streaming architectures, service interfaces - that connect source treatment-center data, internal platforms, and downstream research and analytics consumers.
• Non-functional requirements. Ensure designs meet scalability, performance, resiliency, security, privacy, and regulatory requirements, applying appropriate architectural tactics and documenting the trade-offs behind each decision.

Validation, Governance & Vendor Partnership

• Independent assurance. Serve as a trusted second set of eyes on the Integration Services consultant's designs and deliverables - reviewing, challenging, and validating architecture against CIBMTR's standards, data realities, and long-term strategy.
• Architecture governance. Lead or contribute to an architecture review process that evaluates new solutions for alignment with the target state, security and compliance requirements, and enterprise standards.
• Risk & compliance alignment. Partner with cybersecurity and data-governance stakeholders to ensure architectures honor data-protection, privacy (e.g., HIPAA / PHI handling), data-use-agreement, and model-risk considerations appropriate to a clinical research registry.
• Proofs of concept. Define, guide, and evaluate POCs and technical demonstrations that de-risk key decisions before full implementation.

Mentorship & Team Upskilling

• Capability building. Mentor and upskill data engineers, BI/analytics engineers, analysts, and other IT staff as they transition from a traditional SQL data-warehouse practice to AI-native and cloud-native ways of working.
• Patterns & e