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.

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

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 Power Backend Engineer will develop and maintain data scrapers, enhance the product, and collaborate on roadmap discussions while ensuring API performance.

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.

Perform physical build, maintenance and troubleshooting in multiple data centres: rack-and-stack, cabling (fiber/copper), device migrations, hardware installs, circuit testing, inventory and power monitoring. Provide on-call support, travel across APAC/EMEA/AMRS, and coordinate with network, systems, and project teams to deliver infrastructure projects.

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.

Support and optimize large-scale Spark/YARN jobs and storage (S3/EMR). Build job-profiling, cost-analysis, AI-assisted automation, monitoring/alerting (Prometheus, Flink/Spark), and internal ops tooling to improve stability and reduce costs.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Design, develop, and maintain high-performance, highly available backend services and APIs for Big Data products using Java/Python. Manage full service lifecycle, capacity planning, profiling/benchmarking, troubleshooting, security, and system architecture. Contribute to open-source big data projects and build scalable tools for global operations.

Design and build high-throughput reconciliation systems and data pipelines. Implement real-time (Flink) and batch (Hive) workflows, write complex SQL for validation and anomaly detection, collaborate with Finance/Risk/Product on reconciliation rules, optimize for low latency, and leverage AI/LLM tooling to automate triage and improve operations.

Lead design and implementation of scalable, high-performance backend services for traditional and AI-powered search. Own search architecture, optimize indexing/retrieval/ranking, build reusable frameworks, collaborate with data scientists on LLM-enabled search and agents, and deliver robust, low-latency solutions.

Design and develop high-performance backend services for an AI-powered trading assistant: build AI agent execution, trading order execution, AI sub-account and API key lifecycle management, integrate Binance trading APIs, implement skills/tool-calling frameworks, and ensure scalable, low-latency real-time data pipelines, observability, and security.

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.

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 Software Engineer will be responsible for data collection to support model training, manage data ingestion pipelines, and collaborate with AI team members on data strategy.

The Software Engineer will enhance data collection methods for machine learning, manage cloud infrastructure, and collaborate with AI scientists to improve data quality and efficiency.

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.

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.