Top Data Engineer Jobs in Japan

Lead backend data platform engineering and manage functional data ownership for Japan and Korea, focusing on scalable cloud-native platforms and data pipelines.

Drive improvements in labor productivity and operational efficiency through program management, data-driven insights, and automation solutions in semiconductor operations.

Design, build, and operate data pipelines, warehouses, and traceable databases for hydrogen software products; ensure data quality, traceability, and API integration; collaborate with cross-functional teams and third parties to support reporting, analytics, and product development.

The Data Engineer will design and implement scalable data architectures, collaborate with cross-functional teams, and manage data processing and analysis across multiple domains.

The role involves managing data engineering projects to support AI-driven media solutions, ensuring quality data flow and integration across platforms.

The Data Engineer will develop automated systems for data extraction and formatting, optimize data processing, and facilitate tool development for easy data validation and reporting.

The Data Engineer will analyze large datasets for ADAS/AD software, manage data collection, validate AI performance, and support cross-department collaboration. The role requires experience in ADAS systems and AI solutions.

The Software Engineer will build data ingestion pipelines, manage cloud infrastructure, and collaborate on AI dataset roadmaps for model training operations.

The Software Engineer will enhance data collection and ingestion pipelines, optimize cloud infrastructure, and collaborate on AI datasets for Speechify's products.

Design and operate high-performance data backend systems at scale, focusing on workflow optimization, reliability, and collaboration across teams.

As a Staff Software Engineer focusing on Data Backend, you will design APIs, manage data warehouses, develop data pipelines, and optimize operations using cloud technologies.

As a Software Engineer, you will develop a platform for creating and managing labeled datasets, collaborate with engineers, and deliver scalable data solutions.

Lead design, development, deployment, and continuous improvement of cloud-native data and analytics solutions on Azure. Act as a hands-on technical lead building scalable, secure end-to-end data platforms using Spark/Databricks, Python/PySpark, and orchestration tools. Ensure CI/CD, DevOps, automation, code quality, mentor team members, resolve complex technical issues, and collaborate with stakeholders and vendors to drive platform evolution.

Build, deploy, and support cloud-native data and analytics solutions on Azure. Design and maintain data pipelines, ingestion, transformation and integration processes using Apache Airflow. Ensure data quality, monitoring, CI/CD and infrastructure-as-code. Troubleshoot platform issues, collaborate with cross-functional teams, and contribute to platform improvements and documentation.

Own and maintain analytics instrumentation and data pipelines using Segment, ensuring reliable event tracking to Mixpanel, GA4 and AppsFlyer. Use Terraform for version-controlled analytics infrastructure, monitor and audit data quality, maintain schema documentation, translate business needs into tracking, and configure mobile/web attribution and deep-linking.

Design, build, and operate large-scale distributed data pipelines and platforms (Spark, Kafka, data warehouses/lakes). Optimize performance, establish architecture and tooling, and support ETL, deployment, monitoring, and scalability.

The role involves sourcing audio data for model training, operating cloud infrastructure in GCP, collaborating on data pipelines, and improving data quality and scale for AI models.

The Specialist will provide solutions, demonstrate products, answer technical questions, conduct training, and create marketing materials while collaborating with various departments to drive product sales and enhance customer experience.

The role involves enhancing data ingestion pipelines by sourcing audio data, managing cloud infrastructure on GCP, and collaborating with the AI team to optimize dataset creation for model training.