The TikTok Privacy and Security (PnS) R&D team creates privacy solutions that are intuitive for users, auditable for regulators, and sustainable for the company, making TikTok a platform where people can safely discover, create, and connect.
We build user-facing privacy features, including privacy settings, consent management, data access and export, and age-appropriate experiences.
We protect personal data across its full lifecycle, from collection and transfer through access, storage, retention, and deletion.
We develop the platforms, technical controls, and governance tools behind privacy protection and regulatory compliance in global markets, and we bring privacy-enhancing technologies and research into production at scale.
We tackle challenges unique to operating a platform at global scale: translating rapidly evolving laws and policies into reliable technical controls, providing transparency and auditability across highly complex data systems, supporting different regulatory requirements across regions, and making privacy protection work at the quality and speed production systems demand.
You will work across product privacy, data lifecycle management, data protection and residency, privacy engineering platforms, AI governance, and privacy-preserving technologies, using techniques including automated compliance enforcement, data lineage and taxonomy, differential privacy, trusted execution environments, AI fairness, and privacy protection for generative AI.
The systems you build will protect hundreds of millions of people, and the privacy decisions you make will reach every one of them.
TikTok's Privacy and Security (PNS) Data Retention team makes sure that data across the platform is kept only as long as it is needed, and is then deleted reliably and verifiably.
The data covers online services, offline warehouses, logs, backups, and ML training datasets.
Global privacy rules keep getting stricter, and the data estate keeps growing to exabyte scale.
The team owns the policies, platforms, and technical controls that turn retention commitments into guarantees we can enforce and audit.
As a Backend Engineer - Data Retention, you will design and build scalable, reliable, high-performance systems that manage the lifecycle of data at TikTok scale.
That includes finding and classifying data, applying retention policies, running deletion, and proving that deletion happened.
You will turn fast-changing privacy and regulatory requirements into concrete platform capabilities.
You will work closely with storage infrastructure, data platform, ML platform, product, privacy, legal, and security teams to ship solutions across thousands of services and storage systems.
Responsibilities:
- Design and build large-scale data lifecycle management systems that enforce retention policies across heterogeneous storage: RDBMS, KV stores, caches, message queues, data warehouses (Hive/HDFS), object storage, logs, and backups.
- Build and run high-throughput deletion pipelines, such as TTL enforcement, user account deletion propagation, and partition and snapshot expiry.
They must be correct, idempotent, and observable, and must not hurt the stability or performance of production systems.
- Build data discovery and inventory capabilities, including metadata collection, data lineage, and classification of personal and sensitive data.
These keep retention policies accurately mapped to real storage assets.
- Build verification, auditing, and reporting systems that prove deletion is complete and on time. Provide evidence for internal audits and regulator inquiries.
- Support exception workflows such as legal holds, retention extensions, and policy approvals, with fine-grained access control and full audit trails.
- Work with ML platform teams to extend retention and deletion controls to training datasets, feature stores, and derived artifacts such as embeddings and model checkpoints.
- Work with privacy, legal, and compliance teams so that systems meet applicable regulations (GDPR, CCPA/CPRA, and other global and regional privacy laws) and TikTok's own data commitments.
- Help shape the technical roadmap for the data retention platform, and find and evaluate new techniques and tools that improve coverage, efficiency, and reliability at scale.