Sr. Architect for Biometrics

Role : Sr. Architect for Biometrics

Duration : Long-Term

Location : Remote

Actual JD:

Job Title: Senior Architect - Biometric Data Platforms & Governance

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

Preferred

• 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