Data Engineer Data Application

TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. TikTok has global offices including Los Angeles, New York, London, Paris, Berlin, Dubai, Singapore, Jakarta, Seoul, and Tokyo.

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, and empower strategy execution.

We are looking for passionate Data Engineers that have strong problem-solving skills to join forces with talented cross-functional partners (business operation, data science, engineering, and product management) to solve some of the most interesting data challenges with efficiency and quality. In this role, you will contribute to the company's core business across innovative advertising products, campaign management, and measurement solutions. You will see a direct impact from your day-to-day work to customer satisfaction and company growth.

Responsibilities:

• 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.
• Evaluate, implement, and maintain data infrastructure tools and technologies to support efficient data processing, storage, and query.
• Design, build, and optimize scalable data pipelines to ingest, process, and transform large volumes of data.
• Design and implement robust data models and visualization to support complex analytical queries and reporting requirements.
• Ensure the data integrity, accuracy, and consistency of data by implementing data quality checks, validation processes, and monitoring mechanisms.
• Continuously optimize data pipelines, queries, and processes to improve performance, reduce latency, and enhance scalability.
• Provide rapid response to SLA on-call support to business-critical data pipelines.
• Create and maintain good documentation for data assets and promote best practices for data governance within the data user community.