Staff Software Engineer, Conversion Data Privacy

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).

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.

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.