Database Architect at KYYBA Inc in St. Paul, MN

Job description

• This is a non-exempt position.
• ***'s Electrophysiology (EP) R&D organization generates an expanding volume of clinical, imaging, mapping, and outcomes data that serves as the foundation for next-generation AI/ML solutions. To accelerate innovation, we are seeking a Data Operations & R&D Data Management Manager to lead the strategy, processes, and infrastructure that enable secure, scalable, and compliant use of data across the product development lifecycle.
• This role combines data platform leadership with operational execution. The successful candidate will oversee data ingestion, transfer, cataloging, governance, standardization, quality, and accessibility while partnering closely with R&D engineers, Clinical teams, IT/Cloud organizations, field personnel, physicians, external partners, and vendors. The role will ensure that clinical and field-derived datasets are readily available, trustworthy, compliant, and optimized for AI/ML development, validation, and regulatory submission activities.

Responsibilities

• Data Strategy, Platform, and Infrastructure
• Define and execute a roadmap for modernizing the EP R&D data ecosystem through scalable, searchable, and automated data management capabilities.
• Design and maintain data architecture, metadata standards, clinical dictionaries, and common data models spanning cardiac mapping, imaging, and clinical datasets.
• Partner with IT, Cloud, and engineering teams to implement and enhance data infrastructure leveraging Azure, Databricks, and related enterprise platforms.
• Evaluate and implement tools supporting data cataloging, governance, version control, lineage, labeling, storage, and AI/ML workflows.
• Ensure data systems effectively support both Windows and Linux-based development environments.
• Data Operations and Dataset Readiness
• Coordinate end-to-end acquisition, transfer, ingestion, validation, and management of clinical and field-derived datasets.
• Establish processes to ensure completeness, integrity, traceability, and accessibility of data throughout its lifecycle.
• Partner with physicians, clinical sites, field teams, and data owners to support strategic data collection and partnership initiatives.
• Manage data transfer activities, issue resolution, status tracking, and stakeholder communications to ensure timely delivery of datasets.
• Collaborate with engineering and AI/ML teams to address data quality issues, missing metadata, ingestion failures, and standardization gaps.
• Support dataset versioning, lineage tracking, reproducibility, and audit readiness for AI/ML model development and validation.
• AI/ML Data Enablement
• Work directly with AI/ML and R&D engineering teams to understand data requirements and accelerate model development efforts.
• Build and improve processes that enable efficient discovery, access, preparation, and validation of datasets.
• Oversee labeling and annotation programs, including external vendor management, quality controls, acceptance criteria, and development of gold-standard datasets.
• Drive continuous improvement initiatives that reduce time spent locating, preparing, and validating data.
• Governance, Compliance, and Quality
• Establish and maintain data governance practices covering access controls, retention, stewardship, quality, and security.
• Coordinate reviews with Cybersecurity, Privacy, Legal, OEC, Clinical, and R&D stakeholders to ensure compliant data handling.
• Ensure alignment with corporate policies and applicable regulatory requirements, including FDA, GxP, HIPAA, GDPR, and AI/ML validation expectations.
• Maintain audit-ready documentation, data lineage records, SOPs, work instructions, approvals, and governance artifacts.
• Implement data quality monitoring, validation checks, and remediation processes to ensure ongoing data integrity.
• Cross-Functional Leadership
• Serve as the primary point of coordination among R&D, Clinical, IT/Cloud, AI/ML, field teams, and external partners.
• Translate technical, clinical, and business requirements into actionable data solutions and operational plans.
• Communicate strategy, priorities, progress, risks, and trade-offs to leadership and stakeholders across the organization.
• Manage external vendors, statements of work, timelines, deliverables, service levels, and quality expectations.

Requirements

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Health Informatics, Biomedical Engineering, or a related discipline.
• 5+ years of experience in data management, data operations, data engineering, clinical data support, or related roles.
• Demonstrated experience managing large and diverse datasets, including clinical, imaging, mapping, or time-series data.
• Experience implementing data governance, metadata management, cataloging, and data quality processes.
• Experience coordinating data transfers, data lifecycle activities, or multi-stakeholder data initiatives.
• Knowledge of cloud-based data platforms such as Azure, Databricks, or comparable environments.
• Strong understanding of data privacy, security, compliance, and regulated industry requirements.
• Excellent communication, stakeholder management, and cross-functional leadership skills.
• Strong organizational, project management, and execution capabilities.
• Working knowledge of SQL and experience with scripting or programming languages such as Python.
• Experience within medical devices, healthcare, life sciences, or other regulated R&D environments.
• Familiarity with electrophysiology, cardiac mapping, and clinical imaging data.
• Knowledge of clinical and imaging standards including DICOM, HL7, and FHIR.
• Experience supporting AI/ML development through dataset preparation, annotation, validation, MLOps, or model governance activities.
• Experience with data governance tools such as Microsoft Purview, Collibra, or Alation.
• Knowledge of FDA regulations, 21 CFR Part 11, GxP requirements, HIPAA, and GDPR.
• Experience building or modernizing enterprise data management platforms and processes.
• Experience managing external data providers, labeling vendors, and strategic data partnerships.

Skills

Required

• Data management
• Data operations
• Data engineering
• Clinical data support
• Data governance
• Metadata management
• Data cataloging
• Data quality
• Data transfer coordination
• Data privacy and security
• Compliance
• Communication

Preferred

• Medical devices
• Healthcare
• Life sciences
• Regulated R&D environments
• Electrophysiology
• Cardiac mapping
• Clinical imaging data
• DICOM