Staff Software Engineer - Conversion Data Privacy
Team & Mission
The Privacy & Conversion Data team is responsible for how the company safely and compliantly uses conversion data to power monetization. We build and operate the core privacy infrastructure behind ads reporting and optimization, including controlled data environments, fine‑grained access controls, centralized privacy rules enforcement, and de‑identification pipelines for conversion data. Our mission is to make conversion data privacy‑preserving by default—centralized, de‑identified, auditable, and easy for teams to use, while maintaining high utility for advertisers and staying ahead of an evolving global regulatory landscape.
Role Summary
We’re seeking a Staff Engineer to lead the architecture and technical direction for the conversion data privacy platform, spanning both core Conversion Data systems and de‑identification for ads reporting. You’ll own the end‑to‑end design and evolution of privacy‑critical pipelines and services, partner closely with Product, Data Science, Legal, and infrastructure teams, and set the technical bar for how we use conversion data safely at scale.
What you’ll do:
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.
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.
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.
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.
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.
Partner cross‑functionally with ads, data, product, legal, and infrastructure stakeholders to translate legal/privacy requirements into technical designs, make clear trade‑offs between privacy and utility, and drive alignment on roadmaps, launches, and policy changes that impact advertisers and users.
Mentor and uplevel engineers across multiple teams, lead critical design and code reviews in privacy‑sensitive areas, and establish best practices and documentation for privacy‑by‑design, de‑identification, and large‑scale data systems.
What we’re looking for:
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 with modern big data ecosystems is a plus.
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.
In-Office Requirement Statement:
We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
#LI-REMOTE
#LI-KK6
About the job
Apply for this position
Staff Software Engineer - Conversion Data Privacy
Team & Mission
The Privacy & Conversion Data team is responsible for how the company safely and compliantly uses conversion data to power monetization. We build and operate the core privacy infrastructure behind ads reporting and optimization, including controlled data environments, fine‑grained access controls, centralized privacy rules enforcement, and de‑identification pipelines for conversion data. Our mission is to make conversion data privacy‑preserving by default—centralized, de‑identified, auditable, and easy for teams to use, while maintaining high utility for advertisers and staying ahead of an evolving global regulatory landscape.
Role Summary
We’re seeking a Staff Engineer to lead the architecture and technical direction for the conversion data privacy platform, spanning both core Conversion Data systems and de‑identification for ads reporting. You’ll own the end‑to‑end design and evolution of privacy‑critical pipelines and services, partner closely with Product, Data Science, Legal, and infrastructure teams, and set the technical bar for how we use conversion data safely at scale.
What you’ll do:
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.
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.
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.
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.
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.
Partner cross‑functionally with ads, data, product, legal, and infrastructure stakeholders to translate legal/privacy requirements into technical designs, make clear trade‑offs between privacy and utility, and drive alignment on roadmaps, launches, and policy changes that impact advertisers and users.
Mentor and uplevel engineers across multiple teams, lead critical design and code reviews in privacy‑sensitive areas, and establish best practices and documentation for privacy‑by‑design, de‑identification, and large‑scale data systems.
What we’re looking for:
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 with modern big data ecosystems is a plus.
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.
In-Office Requirement Statement:
We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
Relocation Statement:
This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
#LI-REMOTE
#LI-KK6