Data Engineer-Data Platforms - Japanese Bilingual

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

As a Data Engineer specializing in Data Platforms on AWS, you will advise on, develop, and maintain data engineering solutions on the AWS Cloud ecosystem. You will design, build, and operate batch and real-time data pipelines using various AWS services. 

Your primary responsibilities will include: 

• Design and Develop Data Pipelines: Design, build, and operate batch and real-time data pipelines using AWS services such as AWS EMR, AWS Glue, Glue Catalog, and Kinesis, ensuring seamless integration and operation of data engineering solutions. 

• Create Data Layers: Create data layers on AWS RedShift, Aurora, and DynamoDB, and migrate data using AWS DMS. 

• Develop Batch and Real-Time Pipelines: Develop batch and real-time data pipelines for Data Warehouse and Datalake, utilizing AWS Kinesis and Managed Streaming for Apache Kafka. 

• Manage Data Services: Schedule and manage data services on the AWS Platform, ensuring seamless integration and operation of data engineering solutions. 

• Apply Open Source Technologies: Utilize open source technologies like Apache Airflow and dbt, Spark / Python or Spark / Scala on AWS Platform to support data engineering solutions.

• Exposure to AWS Toolset: Experience working with AWS services such as AWS EMR, AWS Glue, Glue Catalog, and Kinesis to design, build, and operate batch and real-time data pipelines. 

• Data Pipeline Development: Exposure to developing batch and real-time data pipelines for Data Warehouse and Datalake, utilizing AWS Kinesis and Managed Streaming for Apache Kafka. 

• Data Layer Creation: Experience working with AWS RedShift, Aurora, and DynamoDB to create data layers and migrate data using AWS DMS. 

• Open Source Technologies: Exposure to utilizing open source technologies like Apache Airflow and dbt, Spark / Python or Spark / Scala on AWS Platform to support data engineering solutions. 

• Data Service Management: Experience working with scheduling and managing data services on the AWS Platform to ensure seamless integration and operation of data engineering solutions.

• Proficiency with AWS Databrew: Experience working with AWS Glue Databrew to support data engineering solutions, including data preparation and data quality. 

• Knowledge of Lambda Functions: Exposure to using Lambda functions with Python to support data engineering solutions, including data processing and data integration. 

• Familiarity with RedShift Spectrum: Experience working with RedShift Spectrum to support data engineering solutions, including data warehousing and data analytics.