Senior Data Storage & Real-Time Compute Architect

TikTok; Seattle (WA); On-site; USD 242,000 - 456,000; Full time

Benefits offered by this job

Medical insurance

Dental & Vision

401(k) match

Parental leave

Disability insurance

Life insurance

Wellbeing benefits

Paid holidays

Paid sick days

Paid personal time

Job summary

TikTok is seeking a skilled data storage architect to join our Seattle-based team, contributing to offline and real-time data architectures for the recommendation, search, and advertising ecosystems.

You will design scalable storage systems using Paimon and Flink, optimize performance, and ensure reliability across massive-scale data processing. Collaboration with cross-functional teams is essential, with a focus on robustness and efficiency.

Qualifications

• Bachelor's degree or higher in CS/SE or related field with scalable systems experience.
• Strong understanding of Paimon and Flink at source-code level.
• Experience with at least one data lake tech (e.g., Paimon) with practical implementation.
• Proficiency in Java, C++, and Scala with strong coding skills.
• Experience in data warehouse modeling for complex business scenarios.

Responsibilities

• Design and implement offline and real-time data architectures for large-scale systems using Paimon and Flink.
• Build flexible, scalable storage and compute models with Paimon as foundation and Flink computing power.
• Troubleshoot production systems and design mechanisms for data consistency and recovery.
• Develop distributed storage and processing frameworks to support massive data and large-scale business apps.

Skills

Java

C++

Scala

Scalable systems design

Education

Bachelor's degree in CS or related

Tools

Paimon

Flink

Hive

HBase

Kudu

Job description

TikTok is seeking a skilled data storage architect to join our Seattle-based team, contributing to offline and real-time data architectures for the recommendation, search, and advertising ecosystems.

You will design scalable storage systems using Paimon and Flink, optimize performance, and ensure reliability across massive-scale data processing. Collaboration with cross-functional teams is essential, with a focus on robustness and efficiency.