Top Data Engineer Jobs in Tokyo

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, implement, and maintain highly scalable data pipelines and reusable ingestion/validation frameworks using Scala/Spark/Java. Collaborate on requirements, build CI/CD and test automation scripts, support incident recovery, and optimize distributed systems. Work with cloud services, databases, and SQL to enable large-scale data processing and transformations.

Design, build, and maintain ETL/ELT pipelines on Microsoft Fabric; develop Power BI semantic models and reports; integrate enterprise data with AI platforms via MCP; support data governance, quality, lineage, and collaborate with stakeholders to deliver compliant data solutions for commercial and medical insights.

Design, build, and operate petabyte-scale, distributed data pipelines for multimodal robotics datasets. Create storage schemas, query interfaces, data-quality metrics, and tooling to enable researchers to discover, consume, and reproduce VLA training datasets. Collaborate with research teams to iterate on pipelines and monitoring.

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.

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.

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

Design, build, and operate scalable data infrastructure and experimentation platforms for mobile games. Develop batch and streaming pipelines, cloud data platforms, telemetry systems, experimentation capabilities, and AI-assisted engineering guardrails. Lead architecture, technical direction, cross-team delivery, and operational ownership while mentoring engineers and potentially managing the team.

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.

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.

Designs, develops, and maintains secure enterprise data pipelines and ETL/ELT processes integrating government and commercial systems. Ensures data quality, performance, availability, and real-time delivery for logistics, mission support, analytics, and operational decision-making. Responsibilities include database and pipeline optimization, cloud migration support, technical documentation, cybersecurity and governance compliance, troubleshooting, stakeholder coordination, operational exercise support, and mentoring junior personnel.

Build, automate, and operate enterprise-scale data platforms using Databricks, Snowflake, Microsoft Fabric, Kubernetes, and cloud technologies. Responsibilities include Infrastructure as Code, CI/CD, GitOps, platform provisioning, reliability engineering, observability, security, governance, incident remediation, performance optimization, and FinOps. The role supports multi-environment deployments and self-service capabilities for data engineering and analytics teams.

Lead Customer 360 data engineering initiatives across Customer and Sales domains. Design data integration, ingestion, curation, governance, quality, harmonization, and common data models. Build scalable cloud-based data capabilities and integration pipelines while supporting data products, use cases, and analytics. Collaborate with business owners, regional partners, and technology teams to define strategy, roadmaps, prototypes, and MVPs, serving as a subject matter expert in Customer 360 data.

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, and delivery of cloud-native, microservice-based software products. Drive DevSecOps/CI-CD practices, API/system integration, quality assurance, customer-facing solution design, project leadership, and adoption of AI/IoT/digital-twin 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.

Lead design and implementation of complex data architectures and pipelines, ensure data quality through validation and cleansing, build/storage and transformation solutions for analysis, optimize for performance and scalability, collaborate with cross-functional teams, and mentor junior engineers.

Lead design and implementation of complex data systems and architectures. Build and maintain data pipelines and storage, ensure data quality through validation and cleansing, implement transformations for analytics, optimize for performance and scalability, collaborate with cross-functional teams, and mentor junior engineers.

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.

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.