Data Product Manager, AI Data Platform

About the Team

The success of TikTok's data business model hinges on the supply of a large volume of high quality labeled data that will grow exponentially as our business scales up. The Data Solutions Team uses structured and unstructured data to guide and uncover insights, turning our findings into real products to power exponential growth. Data Solutions Team responsibility includes infrastructure construction, recognition capabilities management, global labeling delivery management.

About the Role

We are looking for a Data Product Manager to own and evolve the data infrastructure layer of our AI data annotation platform. This role bridges product management with data engineering — you will define the product vision for data pipelines, data quality frameworks, RBAC governance, and scalable data delivery systems while being hands-on enough to understand schema design, pipeline orchestration, and data quality checks. You will translate complex data requirements into standardized, reliable, and secure data products that enable downstream labelling operations at scale.

Responsibilities

As a data product owner, what you will do: - Own the product strategy and roadmap for data infrastructure — including data ingestion, transformation, quality assurance (DQC), delivery pipelines, and metric governance - Define data asset standards: unified metric dictionaries, pipeline coding templates, and data quality SLAs - Design and drive RBAC (Role-Based Access Control) for all data assets, ensuring compliance and security across teams - Translate business labelling requirements into data pipeline specifications; work with data engineers to build and maintain ETL/ELT workflows - Define and prioritize data product features based on business goals, operational efficiency targets, and scalability requirements - Conduct data modeling, define schemas, and ensure standardization across ingestion formats (CSV, SQL-Hive, JSON, API) - Monitor data pipeline reliability, throughput, and SLA adherence; drive root-cause analysis and continuous improvement - Partner with cross-functional teams (Engineering, Ops, QA, Platform PM) to ensure data readiness for feature modules