Help shape how we collect, process, store, access, and delete data across services, telemetry, analytics, support tooling, third-party integrations, and emerging AI/ML solutions—translating privacy requirements into durable engineering controls
Work on privacy engineering problems where data scale and data systems matter: pipelines, telemetry/analytics, and data stores that support business needs while maintaining strong privacy protections
Build practical controls for data access governance and obfuscation in large datasets (policy enforcement, row/column controls, masking/tokenization, privacy-aware query patterns)
Improve retention/deletion and logging/telemetry hygiene so privacy remains strong as systems evolve
Help enable privacy-safe AI/ML use by implementing controls, infrastructure, and analysis that reduce data exposure and support responsible product development
Collaborate across teams to make privacy the default through templates, guardrails, and automation

Requirements

5+ years of experience in software engineering, data engineering, or data analytics at SaaS companies, with a strong emphasis on data ingestion, governance, and pipeline processing
Demonstrated expertise building and operating production systems at meaningful scale, including debugging, reliability, and operational ownership
Experience implementing data access control and data obfuscation layers on top of data lakes or large analytics environments, including policy-based access, row/column-level controls, tokenization/masking, and privacy-aware query patterns
Experience implementing these controls via commodity governance/authorization offerings (e.g., Databricks Unity Catalog, Okera, Privacera, or similar technologies), including integration into real-world data workflows and enforcement paths
Experience performing analytics and investigations using Python and SQL (e.g., validating data minimization, measuring collection changes, auditing datasets, and supporting privacy reviews)
Experience building or supporting privacy-safe controls, infrastructure, and analysis for AI/ML solutions (e.g., data provenance and curation, access controls around training/evaluation datasets, inference telemetry hygiene, retention/deletion alignment, and practical mitigations for leakage risk)
Familiarity with DLP-style controls and privacy-aware analytics patterns
Proficiency in one or more backend languages (e.g., Go, Rust, Java, TypeScript) and a track record of delivering production-quality code
Practical privacy engineering experience implementing controls such as minimization, access controls, encryption, retention/deletion, and privacy-safe analytics/telemetry
Ability to translate privacy requirements (GDPR / CCPA / CPRA concepts) into engineering work without relying on “paper compliance”
Strong cross-functional communication skills and comfort partnering with Product, Legal/Privacy, Security, Data, and Engineering teams

Benefits

immediate participation in 1Password's benefits program (health, dental, 401k and many others)
utilization of our generous paid time off
an equity grant
participation in our incentive programs

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringdata analyticsdata ingestiondata governancepipeline processingdata access controldata obfuscationPythonSQLbackend programming

Soft Skills

cross-functional communicationcollaborationproblem-solvingoperational ownershipdebuggingreliabilityprivacy engineeringtranslating requirementspartneringautomation