Data Engineer Graduate (Monetization Data)

About the Role

The Monetization Data team builds the data foundation that powers TikTok's global advertising and monetization products. We design and operate large-scale batch and real-time data pipelines, data warehouses, and analytics platforms that help improve advertiser experience, measure business performance, support experimentation, and drive product and strategy decisions.

Key Responsibilities

• Design, build, and maintain scalable batch and real-time data pipelines to process large-scale user behavior, advertising, and monetization data.
• Develop reliable data models, datasets, and metrics that support ads ranking, measurement, experimentation, business analytics, and strategic decision-making.
• Partner with product managers, data scientists, analysts, and engineering teams to understand data needs, define success metrics, and deliver actionable insights.
• Improve data quality, observability, latency, cost efficiency, and platform reliability across large-scale distributed systems.
• Contribute to the evolution of big data infrastructure and computing platforms, including Spark, Flink, Hive, Kafka, and related internal systems.

Requirements & Tech Stack

• Strong programming skills in at least one general-purpose language such as Python, Java, or Go.
• Strong SQL skills and understanding of relational databases, data modeling, or data warehousing concepts.
• Familiarity with distributed data processing or storage systems such as Spark, Flink, Hadoop, Hive, Kafka, Presto/Trino, or similar technologies.
• Strong problem-solving skills, data-driven thinking, and ability to use data to identify issues, form hypotheses, and validate solutions.
• Ability to work collaboratively in a fast-paced, cross-functional engineering environment.

Qualifications

• Individuals who are completing or have recently completed a Bachelor's or Master's degree in Computer Science, Computer Engineering, Data Science, Statistics, Mathematics, or a related discipline.
• Preferred Qualifications: Internship, research, coursework, or project experience in data engineering, backend engineering, distributed systems, data analytics, or machine learning infrastructure.
• Preferred Qualifications: Experience building ETL/ELT pipelines, workflow orchestration, data quality checks, dashboards, or analytical datasets.
• Preferred Qualifications: Experience with advertising technology, recommendation systems, experimentation platforms, or user behavior data analysis.
• Preferred Qualifications: Familiarity with cloud data platforms, lakehouse architectures, or modern data tools such as Airflow, dbt, Iceberg, Delta Lake, Snowflake, BigQuery, Redshift, or Databricks.
• Preferred Qualifications: Experience using AI-assisted development tools, LLM applications, or agentic workflows to improve engineering productivity.
• Preferred Qualifications: Contributions to open-source projects, technical communities, hackathons, or substantial personal projects.
• Preferred Qualifications: Experience contributing to open-source projects and the community; candidates with experience in big data processing or user behavior data analysis is a plus but not required.

Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.