Data Warehouse Engineer Intern: Real-Time Data at Scale

TikTok; Singapore; On-site; SGD 28,000 - 44,000; Full time

Job summary

TikTok is seeking a Data Warehouse Engineer Intern for a 2027 start to join the Data Platform team supporting TikTok LIVE. You will participate in building offline and real-time data warehouses and work with PB-scale data across hundreds of billions of records.

Design scalable data flows, data warehouses, scheduling systems, and analytics services to lower data usage barriers, ensure stable operations, and maximize data value while learning Spark, Hadoop, Hive, and related technologies.

Qualifications

• Currently pursuing an Undergraduate/ Master's in Computer Science, Software Engineering, or a related technical discipline.
• Solid coding foundation with SQL, Python, Scala, Java, or similar languages.
• Experience with big data tools/frameworks like Spark, Hadoop, Hive, Flink, Kafka, and HBase is preferred.

Responsibilities

• Participate in building offline and real-time data warehouses for the TikTok Live business.
• Work with PB-scale data and support the daily processing of hundreds of billions of user data records.
• Support the full big data lifecycle, including data generation, transmission, modeling, statistical analysis, experiment evaluation, and visualization.
• Design and build scalable data flows, data warehouses, scheduling systems, query engines, data services, analytics systems, process standards, and data tools/products to maximize data value.

Skills

SQL

Python

Scala

Java

Big data

Education

Bachelor’s or Master’s in CS/Software Engineering or related

Tools

Spark

Hadoop

Hive

Flink

Kafka

HBase

Job description

TikTok is seeking a Data Warehouse Engineer Intern for a 2027 start to join the Data Platform team supporting TikTok LIVE. You will participate in building offline and real-time data warehouses and work with PB-scale data across hundreds of billions of records.

Design scalable data flows, data warehouses, scheduling systems, and analytics services to lower data usage barriers, ensure stable operations, and maximize data value while learning Spark, Hadoop, Hive, and related technologies.