Top Data Engineer Jobs in Tokyo

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.

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.

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.

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.

Design and build high-performance Java microservices and data pipelines to parse, transform, and analyze multi-chain on-chain data in real time. Integrate with Kafka, Redis, RDS, and data lake systems; optimize low-latency computation for token prices, swaps, liquidity metrics; ensure fault tolerance, observability, and alignment with long-term data platform strategy.

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.

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 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.

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.

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.