Data Engineer - Data Governance

Responsibilities

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. However, the current cost of data labeling is excessively high. The Data Solutions Team uses quantitative and qualitative data to guide and uncover insights, turning our findings into real products to power exponential growth. The Data Solutions Team responsibility includes infrastructure construction, recognition capabilities management, global labeling delivery management.

About the role
As Data Engineer, you will be working on cutting-edge challenges in the big data and Al industry which requires strong passion and capability of innovation. You will collaborate closely with cross-functional teams to understand business requirements and translate them into technical solutions.
Responsibility
1. Design, build, and maintain robust, scalable, and efficient data pipelines for ingesting, processing, and transforming large volumes of data to meet both immediate and long-term business needs.
2. Define the technical strategy and roadmap for data engineering projects in alignment with business objectives, actively evaluate and bring in industry best practices and state-of-the-art technical approaches, and timely update the strategy according to the rapid change of the industry;
3. Own and drive data engineering projects by leveraging both internal and cross-functional resources, setting meaningful and challenging targets, and achieving them with innovative approaches;
4. Ensure data governance and quality, establishing processes to manage and protect the integrity of data across systems.
5. Translate business requirements into actionable data solutions, maintaining a strong understanding of business needs and using data to generate impactful insights for decision-making.
6. Support business intelligence efforts by designing and implementing data warehousing solutions and building interactive dashboards for real-time business analysis.

Qualifications

Minimum Qualifications
1. Bachelor's or Master's degree in Computer Science, Engineering, or related field;
2. 3+ years of experience (or equivalent) in data engineering, with a strong track record of building and managing large-scale, production-grade data pipelines.
3. Proficiency in Python and SQL for data processing and automation; experience with Java or Scala is a plus.
4. Hands-on experience with big data frameworks such as Apache Spark, Hadoop, Kafka, or similar technologies. Experience in performance tuning and optimization of data pipelines and large-scale distributed systems.
5. Strong knowledge of database and data warehousing concepts, including star schema, dimensional modeling, ETL/ELT frameworks, and schema design.
6. Familiarity with data governance, data quality frameworks, and best practices in data validation, lineage, and metadata management.
7. Experience using data visualization tools such as Tableau, Power Bl, or internal platforms to support business insights.

Preferred Qualifications
1. Familiarity with real-time data processing technologies such as Flink and others.
2. Strong communication and interpersonal skills, with the ability to work effectively with both technical and non-technical stakeholders.