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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Solutions Architect - CIBMTR - **Company:** Medical College of Wisconsin - **Location:** Wauwatosa, WI, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Computing, Cloud Engineering, Information Systems, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Warehousing, Monitoring of Systems, IT Management, Python (Programming Language), Machine Learning, Metadata, Meta-Data Management, Performance Tuning, Standard Sql, Search Technologies, SQL Databases, Systems Integration, Large Language Models, Snowflake, Data Lakes, Information Technology, Data Management, Machine Learning Operations, Virtual Agents, Databricks - **Published:** September 24, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88423819/1 ## About the Role * Fluency in architectural trade-off analysis, reference architectures and patterns, non-functional requirements, and architecture governance. * Strong command of lakehouse and modern data-platform concepts - table formats, ELT/ETL, batch and streaming pipelines, storage and compute optimization, and data products (e.g., Databricks/Delta Lake, or Snowflake). * Proficiency in hyperscaler cloud architecture - primarily AWS - including infrastructure-as-code, networking, managed services, cost management, and FinOps principles; Azure or GCP experience a plus. * Solid understanding of the ML lifecycle and MLOps, plus contemporary GenAI and agentic patterns - orchestration frameworks, vector search, prompt/tool governance, evaluation, telemetry, model monitoring, and explainability. * Working knowledge of data governance, cataloging, lineage, metadata management, and unified access-control and policy enforcement. * Strong SQL and proficiency in at least one general-purpose language (e.g., Python). * Understanding of data security, privacy, and regulatory considerations for sensitive data, including PHI/HIPAA-relevant controls and de-identification approaches. * Translates complex technical concepts clearly for both technical and non-technical audiences. * Works effectively across engineering, analytics, security, project management, and external partners. * Constructively challenges and validates the designs of vendors and internal teams alike. * Balances modernization ambition with delivery risk, operational stability, and cost. Qualifications Appropriate experience may be substituted on equivalent basis. Minimum Required Education: Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field. Minimum Required Experience: 8 years or more in data engineering, software/data architecture, or a closely related discipline, including 3+ years in a solutions, data, or enterprise architect capacity. Preferred Education: Master's degree Preferred Experience: Experience in healthcare, life sciences, clinical research, or a regulated data-sensitive domain; familiarity with clinical/research registries, outcomes data, or EHR-derived data. Demonstrated experience mentoring or providing technical leadership to engineering and analytics teams. Preferred Certification/Licensure(s): AWS Solutions Architect Professional or equivalent hyperscaler certification; Databricks or Snowflake data-platform certification; DAMA CDMP or AI/agentic-architecture certification. Physical Requirements Work requires occasionally lifting moderate weight materials, standing, or walking continuously. Work Environment Occasional exposure to dust, noise, temperature changes, or contact with water or other liquids. Work is performed in an environmentally controlled environment. Sensory Acuity Ability to detect and translate speech or other communication required. May occasionally require the ability to distinguish colors and perceive relative distances between objects. #LI-NK1 ## Description 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 & enablement. Create reference implementations, reusable patterns, standards, and documentation; lead enablement sessions, code/design reviews, and pairing to embed new skills durably in the team. * Knowledge transfer. Ensure that knowledge created with the Integration Services partner is captured internally so CIBMTR can operate and extend the platform independently over time. Strategy, Advisory & Communication * Trusted advisor. Advise IT leadership on architectural direction, sequencing, build-vs-buy decisions, tooling selection, and the practical implications of emerging technologies. * Roadmap contribution. Help shape and maintain the technical roadmap for the multi-year program, balancing modernization ambitions against delivery risk and operational stability. * Stakeholder communication. Translate complex technical concepts for both technical teams and non-technical stakeholders, building shared understanding and alignment across the program. * Perform other duties as assigned. ## Related Videos - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)