Genomic Data Privacy Engineer

About the role

As a Genomic Data Privacy Engineer at DNA Protocol, you will support genomic use cases within a broader privacy-first digital identity platform. Define the boundary between sensitive biological records, credential claims, scoped proofs and public XRPL evidence.

Work with laboratories, bioinformatics engineers and cryptography specialists on ingestion, normalization, storage, access and verification. Prioritize data minimization and documented provenance; private source records stay off-chain, and public commitments must not be treated as automatic anonymity or proof of biological identity.

Responsibilities

• Design ingestion and normalization pipelines for genomic files and associated provenance metadata.
• Define reproducible, versioned transformations from genomic inputs to the limited claims and commitments required by a credential workflow.
• Implement encrypted off-chain storage, scoped access controls, and auditable access events.
• Map consent and permitted-use requirements into technical controls with support from qualified domain and policy specialists.
• Design retention and access-revocation workflows that acknowledge the limits of immutable public records.
• Use synthetic or appropriately authorized datasets for development and document risks in logs, exports, and integration boundaries.

Qualifications

• Experience building secure data pipelines or bioinformatics infrastructure for sensitive datasets.
• Practical understanding of genomic file formats such as FASTA, FASTQ, or VCF and the importance of provenance.
• Strong Python, TypeScript, Go, or comparable engineering skills with attention to reproducibility.
• Experience with encryption, access control, data lifecycle design, and privacy-focused threat modeling.
• Ability to translate domain requirements into clear technical specifications and collaborate with laboratory teams.

Helpful Experience

• Experience with laboratory information systems or research data platforms.
• Familiarity with privacy engineering, de-identification limitations, and re-identification risks.
• Experience working alongside security, governance, or regulatory specialists.

What Success Looks Like

• A documented data flow that identifies every sensitive-data boundary.
• A reproducible pipeline using synthetic or explicitly authorized inputs.
• A practical access and retention model that avoids overstating what on-chain commitments can guarantee.

How to Apply

Send a CV or professional profile and a short note about your interest in this role. Include links to relevant work where appropriate. Please do not send genetic data, patient information, private keys, or confidential employer materials.

Location, engagement type, and compensation for this role are to be confirmed.