Data Engineer/Data Science Engineer - Global Data Supervisory Department (GDSD)

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** Can Refer All Jobs Listed Here, https://japan-job-en.rakuten.careers/search-jobs

Job Description:

Business Overview

Rakuten group has almost 100 million customer base in Japan and 1 billion globally as well, providing more than 70 services in a variety such as ecommerce, payment services, financial services, telecommunication, media, sports, etc.

Department Overview

Global Data Supervisory Department (GDSD) provides data science initiatives by leveraging Rakuten group’s data. The department has international culture created by excellent employees joined around the world and provides the cutting-edge data science. Following the strategic vision “Rakuten as a data-driven membership company”, GDSD is expanding our data activities across our multiple Rakuten group companies.

Position Details

We are looking for a Data Engineer/Data Science Engineer to work within one of the following departments within our Global Data Supervisory Department:

- Data Platform Department

- Marketing AI Department

- AI Products Department

- Data Science Consulting Department

Mandatory Qualifications**:**

- Computer science or related background

- 5+ years’ experience in software development, especially using Python as a programming language

- Experience with common Linux commands and Linux scripting languages

- Experience with Hadoop, MapReduce, HDFS and Big Data querying tools, such as Tez, Hive, and Impala

- Experience with designing and building tools based on specific requirements

- Experience with building and maintaining data science platform

- Experience with SQL and some of the modern relational databases

Desired Qualifications**:**

- Experiences in web service development in multiple industries.

- Solid knowledge of large volumes data processing

- Experience with Big Data ML toolkits, such as Mahout, SparkML, or H2O

- Experience with NoSQL databases, such as HBase, Redis, CouchBase

- Familiar with data mining concepts and machine learning algorithms

- Experience with Spark and stream-processing systems, using solutions such as Storm or Spark-Streaming

- Knowledge of various ETL techniques and frameworks, such as Flume


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