Database Architect

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., * 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 Clienture, 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., * Enable R&D teams to rapidly Client, access, and trust the data required for innovation.
• Establish a scalable data foundation supporting AI/ML-powered products and future clinical applications.
• Improve operational efficiency, compliance, data quality, and regulatory readiness across the EP organization.
• Accelerate the development of next-generation cardiac mapping, imaging, and AI-enabled technologies that improve patient care.

• 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 Clienture, 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.