Data Product Manager, AI Data Platform

Description

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

Requirements

Minimum Qualification(s):
- Bachelor's / Master's degree in Computer Science, Data Engineering, Information Systems, or related technical field
- 3+ years of data engineering or data platform experience, with at least 1 year in a product management or product ownership capacity
- Proficient in SQL (Hive/Spark SQL); working knowledge of Python for data processing and automation
- Hands-on with data pipeline tools (Airflow, Spark, Flink) and ETL/ELT design patterns
- Experience with data governance: RBAC, data quality monitoring, SLA management, data lineage
- Strong understanding of data warehousing, dimensional modeling, and big data architecture
- Strong PRD writing, analytical thinking, and cross-functional influence skills

Preferred Qualification(s)
- Experience with metric/KPI framework design and BI tooling (e.g., Grafana, Tableau, internal dashboards)
- Familiarity with data security and compliance standards (data classification, access auditing)
- Experience building or managing large-scale annotation/labelling data platforms
- Knowledge of ML data pipelines (training data management, golden dataset curation, data sampling strategies)
- Experience with agile delivery methodologies
- Consumer-facing or enterprise SaaS product experience