Staff Data Boundary Architect — Governance & Privacy

Dipp AI Technologies, Inc.; New York (NY); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity (4-year vest)

Medical, dental, and vision coverage

401(k) with company contribution

Hardware budget

Learning & certification budget

Job summary

Dipp Intelligence seeks a senior data security engineer to architect controls ensuring enterprise data never trains external models. You will implement classification, egress policies, tokenisation, redaction, and provider-eligibility checks, building private-tenant inference paths and evidence pipelines to show boundary enforcement.

You'll collaborate with security reviewers and design the Dipp Intelligence layer that preserves tenancy while enabling governed inference.

Qualifications

• Experience with data governance, DLP, or privacy engineering in production systems.
• Strong distributed-systems fundamentals and streaming/pipeline experience.
• Knowledge of GDPR, HIPAA, or sector data-handling rules and how they translate into architecture.
• Experience with tokenisation, encryption in use, or privacy-preserving techniques.

Responsibilities

• Build data classification and tagging that follows payloads through the control plane.
• Implement egress policy, redaction, tokenisation, and provider-eligibility enforcement.
• Design private-tenant inference paths for restricted workloads.
• Produce customer-facing evidence of boundary enforcement, not just assurances.
• Work with security reviewers to validate the boundary under adversarial questioning.

Skills

Data governance

DLP

Privacy engineering

Distributed systems

Streaming pipelines

Tokenisation

Encryption-in-use

Security reviews

Education

BS/MS in CS or Information Security

Job description

Guarantee architecturally that enterprise data never trains anyone else's model.

The Data Control Gateway is where enterprise data meets model inference. Its promise is architectural, not contractual: restricted data does not leave the tenant boundary, and it is never used to train a third-party model.

You will build classification, egress control, tokenisation and redaction, and the provider-eligibility logic that enforces this before a request is made. You will also build the proof: telemetry and evidence showing a customer exactly where their data went and where it could not go.

The counterpart is Dipp Intelligence — the compounding operational value an enterprise gets from its own governed usage, kept inside its own boundary.

What you will do

• Build data classification and tagging that follows payloads through the control plane.
• Implement egress policy, redaction, tokenisation, and provider-eligibility enforcement.
• Design private-tenant inference paths for restricted workloads.
• Produce customer-facing evidence of boundary enforcement, not just assurances.
• Work with security reviewers to validate the boundary under adversarial questioning.
• Build the Dipp Intelligence aggregation layer without compromising tenant isolation.

What we look for

• Experience with data governance, DLP, or privacy engineering in production systems.
• Strong distributed-systems fundamentals and streaming or pipeline experience.
• Knowledge of GDPR, HIPAA, or sector data-handling rules and how they translate into architecture.
• Experience with tokenisation, encryption in use, or privacy-preserving techniques.
• Ability to explain a boundary guarantee precisely, without overstating it.
• Comfort in enterprise security reviews.

Experience

• 6+ years in data platform, security, or backend engineering.
• Has shipped a data governance or egress-control capability that passed external review.
• Experience with multi-tenant isolation in a regulated environment is a strong plus.

Education

• BS or MS in Computer Science, Data Engineering, Information Security, or a related field.
• Equivalent professional experience is accepted in place of a degree.

Compensation and benefits

• Meaningful equity with a standard four-year vest.
• Full medical, dental, and vision coverage.
• 401(k) with company contribution and a hardware budget.
• Annual learning and certification budget.