Role: Senior Architect – Biometric Data Platforms & Governance
Location: Remote
Role Summary
Design and implement a scalable, secure platform for storing, managing, and retrieving biometric images and metadata, and enabling efficient dataset creation for ML model training, including sourcing data from external partners.
Key Responsibilities
Architect end-to-end systems for image ingestion, storage, and retrieval
Design metadata schemas and indexing for fast, flexible search
Build scalable solutions for large-volume biometric data (images + metadata)
Develop pipelines for ML dataset curation (filtering, labeling, versioning)
Enable query APIs and dataset extraction workflows for ML teams
Third-Party Data Sourcing & Governance
Define and manage processes for sourcing data from external partners (universities, research labs, vendors)
Collaborate with internal and external legal teams to:
Establish data-sharing agreements and usage rights
Ensure privacy compliance and consent validation
Enforce governance for every data acquisition lifecycle
Implement controls to verify provenance, licensing, and permitted use of all collected data
Security, Privacy & Compliance
Ensure end-to-end protection of biometric data (encryption, access control, auditing)
Embed privacy-by-design principles and regulatory compliance into all workflows
Performance & Scalability
Optimize for high-throughput ingestion and low-latency retrieval
Ensure system reliability at scale (millions/billions of records)
Qualifications
8+ years in data architecture or distributed systems
Experience with large-scale image/media platforms
Strong expertise in databases (SQL/NoSQL) and object storage
Experience working with legal/compliance processes for sensitive data
Experience with biometric or computer vision systems
Familiarity with third-party data acquisition and governance
Knowledge of vector search / similarity search
Cloud platforms (AWS/Azure/Google Cloud Platform)
Success Criteria
Scalable, performant image + metadata platform
Compliant, well-governed third-party data ingestion
High-quality, audit-ready datasets for ML training
Strong privacy and legal adherence across all data sources
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