Data Engineer Graduate (Monetization Data) - 2026 Start (BS/MS)

TikTok's immersive experience, global presence, and high engagement makes it the ideal marketing destination for business, big and small, to showcase their unique brand identity, connect with their consumers, and build strong lasting relationships over time. The Ads Data Team builds and manages the petabyte scale data infrastructure, batch/realtime pipelines and services to support Tiktok's global Ads business. We are committed to building a robust data foundation and scalable data applications, unblocking the full potential of advertising data to optimize advertiser experience, boost business growth, empower strategy execution.



We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.



Successful candidates must be able to commit to an onboarding date by end of year 2026.



We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume.



Applications will be reviewed on a rolling basis. We encourage you to apply early.



Candidates can apply for a maximum of TWO positions and will be considered for jobs in the order you applied for. The application limit is applicable to TikTok and its affiliates' jobs globally.



Key Responsibilities:

1. Work closely with Product Managers, Data Scientists/Analysts, and Software/Machine Learning Engineers and other stakeholders to understand data requirements and deliver data solutions that meet business needs.

2. Evaluate, implement and maintain data infrastructure tools and technologies to support efficient data processing, storage and query.

3. Design, build and optimize scalable data pipelines to ingest, process and transform large volumes of data.

4. Design and implement robust data models and visualization to support complex analytical queries and reporting requirements.

5. Ensure the data integrity, accuracy and consistency of data by implementing data quality checks, validation processes and monitoring mechanisms.

6. Continously optimize data pipelines, queries and processes to improve performance, reduce latency and enhance scalability.

7. Provide rapid response to SLA oncall support to business critical data pipelines.

8. Create and maintain good documentation for data assets and promote best practices for data governance within the data user community.