Data Lake Storage Engineer: Real-time & Offline Systems

BYTEDANCE PTE. LTD.; Singapore; On-site; SGD 120,000 - 170,000; Full time

Benefits offered by this job

Career growth opportunity

Paid leave

Flat organization

Job summary

ByteDance is hiring for the Data Ecosystem Team to craft and implement storage solutions for offline data within a billion-user recommendation system. The role focuses on reliability, uptime, and scalable performance across diverse data sources and workloads.

You will design distributed databases and offline/real-time data architectures, contribute to a high-performance storage system, and help build tools that empower the data platforms for recommendation, search, and advertising use cases.

Qualifications

• Bachelor's degree or higher in CS or related fields with 3+ years building scalable systems.
• Proficiency in Spark or Flink at the source code level; experience customizing is preferred.

Responsibilities

• Design and develop distributed database HBase-related components.
• Design and development of single-node RocksDB-related components.
• Design offline/real-time data architecture for large-scale recommendation systems.
• Design a flexible, scalable, stable, and high-performance storage system and computation model.
• Troubleshoot production systems and build tools to ensure overall stability.
• Build industry-leading distributed storage, batch, and stream processing frameworks.

Skills

Java

C++

Scala

Spark

Flink

HBase

Education

Bachelor's Degree in Computer Science

Tools

HBase

RocksDB

Parquet

ORC

Hive

Kudu

Job description

ByteDance is hiring for the Data Ecosystem Team to craft and implement storage solutions for offline data within a billion-user recommendation system. The role focuses on reliability, uptime, and scalable performance across diverse data sources and workloads.

You will design distributed databases and offline/real-time data architectures, contribute to a high-performance storage system, and help build tools that empower the data platforms for recommendation, search, and advertising use cases.