Laboratory Data Architect

Title: Laboratory Data Architect – Cellular Therapy Services (CTS)Organization: Boulder Biologics Research Center (BBRC)Department: Cellular Therapy Services (CTS) – Data Systems & ArchitectureLocation: Boulder, Colorado (On-site)Reports To: Laboratory Technical Director (Technical Execution & Laboratory Systems)Works Closely With: Medical Director, Director of Quality & Cellular Therapy Systems, Executive DirectorSalary Range: $180,000-$210,000About the OrganizationBoulder Biologics Research Center (BBRC) is an early-stage, build-phase organization developing a fully integrated laboratory and clinical system for translational cellular therapy.The organization is transitioning from paper-based and fragmented processes into a structured, system-driven environment. This role is central to building that foundation—creating systematic, scalable workflows and integrated systems capable of supporting research, clinical application, and future therapeutic development.This is not a maintenance role. It is a build-and-design role within a startup-like environment, requiring individuals who can create structure where it does not yet exist.Position OverviewThe Laboratory Data Architect is responsible for designing and implementing structured, integrated laboratory workflows and systems across instrumentation, laboratory operations, and software platforms.This role ensures laboratory workflows are systematic, connected, and scalable to support regulatory compliance, operational efficiency, and future analytics and AI use.The position requires a combination of laboratory domain understanding and system architecture expertise. Programming is used as a tool to implement integrations and workflows—not as the primary focus.Core ResponsibilitiesCapture, structure, and connect all laboratory workflows and bioprocesses Ensure workflows are systematic, repeatable, and not dependent on manual processes Design and maintain integrated systems across MES, QMS, LIMS, and data platforms Optimize workflow coordination, scheduling, and operational throughput Structure systems and data to support downstream analytics, regulatory reporting, and AI readiness Key Responsibilities1. Workflow Design and OptimizationDesign and implement standardized, system-driven workflows across laboratory operations Ensure workflows are compliant, repeatable, and scalable Identify inefficiencies and implement system-based improvements Optimize workflow orchestration, scheduling, and coordination across laboratory processes 2. System Integration and ArchitectureDesign and implement API-driven integrations between laboratory instruments, LIMS, MES, QMS, and enterprise systems Configure and integrate laboratory instruments and software platforms into a unified system architecture Establish event-driven or pipeline-based architectures to enable real-time or near-real-time data flow Define canonical data models and integration patterns across systems Ensure reliable, traceable, and consistent data flow across the laboratory environment 3. Data Architecture & ModelingDesign and maintain canonical data models across laboratory systems Define standards for data structures, naming conventions, and schema evolution Ensure full data lineage, traceability, and auditability across systems Structure data to support analytics, regulatory reporting, and future AI/ML use 4. Programming and System DevelopmentDevelop integration services, APIs, and middleware using modern programming languages (e.g., Python) Build and maintain data pipelines and workflow orchestration systems Implement robust error handling, logging, and monitoring across integrations Troubleshoot system issues and implement corrective actions (CAPAs) Ensure systems are reliable, secure, and maintainable 5. Data Structuring & AI ReadinessDesign systems and data structures to enable future analytics and AI use Ensure data is clean, consistent, and properly structured across systems Establish standards for data capture, storage, and integration Enable AI readiness through strong data integrity, accessibility, and traceability 6. Regulated Data & Compliance EnvironmentDesign systems that meet regulatory requirements (GMP, GxP, AABB, CLIA) Ensure adherence to data integrity principles (e.g., ALCOA+) Support system validation, audit readiness, and inspection processes Maintain documentation and traceability across all systems and workflows Role BoundariesNot responsible for performing laboratory operations or clinical procedures Not responsible for primary quality ownership or regulatory decision-making Not responsible for developing or applying AI models Responsible for how systems function—not for scientific or clinical decisionsThis role is primarily focused on laboratory-specific system architecture, workflow design, and data integration. The incumbent may provide supporting assistance with general IT infrastructure tasks as needed; primary ownership of general IT infrastructure resides with the IT Infrastructure Specialist / team.Regulatory and Compliance Responsibilities Ensure adherence to data integrity principles (ALCOA+) and maintain documentation to support audits and inspections Activity participate in Computer System Validation (CSV) activities and support system validation and verificationWork closely with Quality leadership to ensure all systems and workflows meet applicable regulatory requirements and compliance (e.g., (GMP, GxP, AABB, CLIA, 21 CFR Part 11)Support identification, investigation, and resolution of system-related deviations, including implementation of CAPAsQualifications/EducationDegree in Life Sciences or related field Strong preference for candidates with both life sciences background and system/software capability ExperienceExperience working with laboratory systems and instrumentation Strong background in system architecture, integration, or software development Experience designing and implementing API-driven system integrations Experience managing workflows across multiple interconnected systems Experience in regulated environments (GMP, GxP, AABB, CLIA) preferred Ideal candidates combine laboratory domain expertise with hands-on system design and integration capability ·      Distinguished from general IT roles by deep laboratory domain knowledge combined with system architecture expertise.·      Strongly preferred: Experience collaborating with Quality Assurance teams on Computer System Validation (CSV) and CAPA processes in regulated environments·      Technical Skills – Data Architecture & Platform EnvironmentThe Laboratory Data Architect will operate within a modern, integrated data architecture environment. Experience with the following is strongly preferred:Data Platforms / Lakehouse Architectures: Platforms such as Microsoft Fabric, Databricks, or Snowflake Data Storage & Structuring: Relational databases and modern lakehouse storage patterns (e.g., Delta Lake, Iceberg) Workflow Orchestration: Tools such as Apache Airflow or Prefect Data Integration & Connectivity: ELT/ETL pipelines and API-based system integration API & Integration Architecture: RESTful APIs and distributed system integration; familiarity with HL7/FHIR is a plus Data Governance & Quality: Data validation, lineage, audit-ability, and quality tooling