Data Platform Engineer — Build Scalable Big Data

TikTok; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Benefits offered by this job

Medical, dental & vision insurance

401(k) with company match

Paid parental leave

Short/Long-term disability

Job summary

TikTok is seeking a data engineer to join the data platform team. You will design, build, and optimize one of the world’s largest data platforms, touching diverse systems across the data ecosystem. Your work will impact core products and hundreds of millions of users.

You will implement robust data transformations, contribute to scalable big data architectures, and establish design best practices for engineers and non-technical colleagues alike.

Qualifications

• BS or MS degree in Computer Science or related technical field or equivalent practical experience.
• Experience with Big Data technologies (Hadoop, MapReduce, Hive, Spark, Metastore, Presto, Flume, Kafka, ClickHouse, Flink, etc.).
• Experience with data analysis, data ingestion and data integration.
• Experience with ETL and architecting data systems.
• Experience with schema design, data modeling and SQL queries.

Responsibilities

• Design and build data transformations efficiently and reliably for reporting, growth analysis, and multi-dimensional analysis.
• Design and implement reliable, scalable big data systems supporting core products and business.
• Establish solid design and engineering best practices for both engineers and non-technical stakeholders.

Skills

Big Data

ETL design

SQL querying

Data modeling

Kafka

Education

BS or MS in Computer Science or related field

Tools

Hadoop

Spark

Hive

Presto

Flume

Kafka

ClickHouse

Flink

Job description

TikTok is seeking a data engineer to join the data platform team. You will design, build, and optimize one of the world’s largest data platforms, touching diverse systems across the data ecosystem. Your work will impact core products and hundreds of millions of users.

You will implement robust data transformations, contribute to scalable big data architectures, and establish design best practices for engineers and non-technical colleagues alike.