Data Product Manager - AI Data Platform

About the Team

The success of TikTok's data-driven business depends on a robust supply of high-quality labeled data, expected to increase exponentially with business growth. The Data Solutions Team focuses on utilizing both structured and unstructured data to generate insights and develop products that accelerate growth. Their responsibilities cover infrastructure development, recognition capability management, and overseeing global labeling processes.

About the Role

This role involves leading and advancing the data infrastructure layer within TikTok's AI data annotation platform. Serving as a bridge between product management and data engineering, the manager will define visions for data pipelines, data quality systems, role-based access controls (RBAC), and scalable data delivery. The role requires hands-on knowledge of schema design, pipeline orchestration, and quality assurance, transforming complex data requirements into standardized, reliable, and secure data products to power labeling operations at scale.

Key Responsibilities

• Steer product strategy and roadmap for data infrastructure focusing on ingestion, transformation, data quality assurance, delivery pipelines, and metric governance.
• Establish data asset standards such as unified metric dictionaries, pipeline coding templates, and data quality service level agreements (SLAs).
• Design and implement RBAC for all data assets to ensure security and compliance across teams.
• Convert business labeling needs into detailed data pipeline specifications and collaborate with data engineers on ETL/ELT workflows.
• Prioritize data product features aligned with business objectives, operational efficiency, and scalability.
• Perform data modeling, schema definition, and standardize ingestion formats including CSV, SQL-Hive, JSON, and API.
• Monitor and enhance reliability, throughput, and SLA compliance of data pipelines through root cause analysis and continuous improvements.
• Collaborate with Engineering, Operations, QA, and Platform Product Management teams to ensure data readiness for feature modules.

Qualifications

Required:

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related technical discipline.
• At least 3 years of experience in data engineering or data platforms, with a minimum of 1 year in product management or ownership roles.
• Strong proficiency in SQL, including Hive and Spark SQL, and working knowledge of Python for data tasks and automation.
• Practical experience with data pipeline tools like Airflow, Spark, and Flink, alongside ETL/ELT design patterns.
• Expertise in data governance including RBAC, data quality monitoring, SLA management, and data lineage.
• Deep understanding of data warehousing concepts, dimensional modeling, and big data systems architecture.
• Excellent skills in drafting product requirement documents (PRDs), analytical thinking, and influencing cross-functional teams.

Preferred:

• Experience designing metric/KPI frameworks and working with BI tools such as Grafana, Tableau, or internal dashboards.
• Knowledge of data security and compliance practices, including data classification and access auditing.
• Background in building or managing large-scale annotation or labeling data platforms.
• Familiarity with machine learning data pipelines including training data handling, golden dataset curation, and data sampling methods.
• Experience with agile methodologies and consumer or enterprise SaaS products.

Why Join TikTok

TikTok is a leading platform for short-form mobile videos, dedicated to inspiring creativity and spreading joy. With global headquarters in Los Angeles and Singapore, the company operates worldwide with offices across major cities. TikTok values curiosity, humility, impact, and resilience, fostering a collaborative environment where employees can innovate and grow together. The company is dedicated to diversity and inclusion, aiming to create a workplace that reflects and respects its global audience's diverse perspectives and backgrounds.