Top Data Engineer Jobs in Tokyo

Lead design, delivery, and operation of enterprise data platforms and ETL/ELT pipelines for Japan & Korea. Translate data strategy into engineering roadmaps, automate pipelines, ensure CI/CD and platform reliability, integrate local and partner data, manage vendors, and build a high-performing data engineering team while representing engineering in governance forums.

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

Design, own, and lead end-to-end data architecture and pipelines for large-scale multimodal fleet and simulation data. Build canonical datasets, SLAs, monitoring, and self-serve tools to support ML-based motion planning. Mentor engineers, set organization-wide data engineering standards, prototype architectural bets, and collaborate across teams to deliver reliable, discoverable data products.

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

Customer-embedded senior data scientist who translates business needs into technical approaches, builds and ships production-grade solutions, influences senior stakeholders, accelerates time-to-value, and transitions work to FDE teams. Operates in ambiguous, fast-moving environments with end-to-end ownership and industry-tailored solutions.

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 and maintain RESTful APIs, build data warehouses, develop data pipelines, and optimize data operations using cloud platforms.

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 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.

Lead site-based CSA activities for data centre projects: manage contractors, ensure compliance with design, safety and quality, coordinate stakeholders, produce reports, and support project leadership.

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, 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.

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.