Top Data Engineer Jobs in Nagoya

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.

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.