Software Engineer — Data Privacy & Infrastructure

Responsibilities

About the Team. You will join the Tik Tok USDS Data Governance and Assurance Engineering team. We are the architects of the foundational infrastructure that manages the entire data lifecycle for one of the world's most massive data ecosystems. Our mission is to ensure that every byte of US data is handled with the highest standards of integrity, security, and privacy compliance.

About the Role. As a Staff Software Engineer - Data Privacy & Infrastructure, you will be the principal technical anchor and visionary for our Data Lifecycle Management (DLM) and Privacy Engineering initiatives. You will not just implement systems; you will architect and define the long-term technical roadmap for how we achieve 100% visibility into where data lives, how it moves (Lineage), and how it is decommissioned across a massive, heterogeneous ecosystem.

You will lead highly complex, cross-functional technical initiatives that serve as the prerequisite for all AI Safety—safeguarding the integrity of petabyte-scale data while deploying cutting-edge privacy techniques.

• Next-Gen Privacy & PETs Strategy:
  Serving as the key subject matter expert to research, evaluate, and productionalize advanced PETs (such as Differential Privacy, Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multi-Party Computation) to unlock secure, privacy-preserving ML training and analytics.
• Data Inventory & Taxonomy:
  Directing the design of self-healing, automated scanning engines capable of identifying data across global, petabyte-scale Data Lakes and real-time streams with minimal performance overhead.
• Scalable Onboarding & Risk Mitigation:
  Designing highly scalable, self-service frameworks and migration playbooks that allow product teams to onboard new applications into the DLM scope autonomously. You will define clear, tiered risk-mitigation pathways and ensure integration happens with optimal cost-efficiency and minimal developer friction.
• Lineage & Traceability:
  Defining the technical standards and system design for tracking the 'genealogy' of data from ingestion to complex machine learning training sets.
• Automated Remediation:
  Overseeing the engineering of zero-tolerance, high-reliability 'Right to be Forgotten' pipelines that execute near-instantaneous deletion across disparate offline and online storage.

• Technical Leadership & Vision:
  Define the architectural blueprint and long-term technical roadmap for the DLM Platform and PET integration. Elevate our 'Privacy-as-Code' vision from concept to production-grade reality.
• DLM Platform Architecture:
  Lead the design and scaling of high-throughput, fault-tolerant backend services managing data retention, archival, and purging policies across heterogeneous engines (HDFS, Click House, MySQL, etc.).
• Federated Onboarding & Fin Ops:
  Architect developer-friendly, self-service onboarding APIs and tools that minimize engineering friction when bringing new apps into DLM. Continuously optimize the infrastructure cost-of-compliance, balancing data processing overhead against business budget constraints.
• Risk-Mitigated Migrations:
  Define technical standards, fallback mechanisms, and progressive rollout strategies to safely migrate legacy systems and new business domains into governance pipelines without breaking production SLAs.
• Enterprise Metadata & Lineage:
  Drive the architecture of a highly scalable, centralized metadata repository mapping complex data relationships, enabling real-time audits and proactive compliance checks.
• PETs Implementation:
  Champion and build the foundational cryptographic and statistical frameworks (e.g., advanced pseudonymization, differential privacy) needed to protect user identity in large-scale analytics and ML…