Staff Software Engineer, Conversion Data Privacy

Pinterest Pinterest · Consumer · San Francisco, CA · Monetization

Staff Engineer to lead architecture and technical direction for Pinterest's conversion data privacy platform, focusing on safe and compliant use of conversion data for ads reporting and monetization. This involves designing and evolving privacy-critical pipelines and services, including controlled data environments, access controls, privacy rules enforcement, and de-identification pipelines. The role requires partnering with Product, Data Science, Legal, and infrastructure teams to set technical standards for data privacy at scale and mentor other engineers.

What you'd actually do

  1. Lead the technical strategy and architecture for conversion data privacy across access controls, de-identification, deletion, and privacy rules enforcement, driving toward a centralized, de-identified‑by‑default, automated privacy platform for monetization.
  2. Design and evolve core privacy infrastructure including controlled environments for sensitive data, fine‑grained authorization and policy enforcement, and a central policy repository that consistently governs access across major data platforms and query engines.
  3. Own de‑identification pipelines for ads reporting end‑to‑end—from separating sensitive and non‑sensitive data, applying de‑identification techniques and transformations, and generating privacy‑preserving datasets, to validating data utility and feeding reporting and analytics surfaces.
  4. Build and improve privacy frameworks and tooling (for both online and offline workflows) that make safe, compliant conversion data usage simple and self‑service for downstream teams, reducing onboarding friction for new datasets, restrictions, and use cases.
  5. Drive operational excellence and compliance by defining SLAs, building robust monitoring and alerting (e.g., de‑identification quality, opt‑out metrics, data leakages), leading incident response, and developing performant deletion and leakage‑handling workflows that meet regulatory and audit requirements.

Skills

Required

  • BS+ in Computer Science (or related field) or equivalent practical experience
  • 8+ years of professional software engineering experience, with a focus on large‑scale data systems or distributed systems
  • Strong proficiency building and operating data pipelines and services using Java/Scala/Kotlin or Python, plus SQL
  • Experience designing secure, reliable systems and APIs, with solid grounding in data modeling, access control, and performance optimization
  • Meaningful experience in at least one of: privacy‑preserving data systems (e.g., de‑identification, k‑anonymity), ads measurement/attribution, or large‑scale analytics/experimentation platforms
  • Proven ability to drive cross‑team technical initiatives from design through rollout, working closely with product, data science, and non‑engineering partners (e.g., Legal, Compliance)
  • Strong communication and leadership skills, with a track record of mentoring engineers, raising engineering standards, and making sound decisions in ambiguous, high‑impact problem spaces

Nice to have

  • experience with modern big data ecosystems is a plus

What the JD emphasized

  • privacy-preserving data systems
  • de-identification
  • ads reporting
  • monetization
  • regulatory landscape
  • controlled environments
  • fine-grained authorization
  • policy enforcement
  • central policy repository
  • ads reporting end-to-end
  • privacy-preserving datasets
  • privacy frameworks and tooling
  • safe, compliant conversion data usage
  • operational excellence and compliance
  • robust monitoring and alerting
  • regulatory and audit requirements
  • legal/privacy requirements
  • privacy and utility
  • privacy-by-design
  • de-identification
  • large-scale data systems