Top Data Engineer Jobs in Tokyo

Develop, test, deploy, and support Azure-based ETL/ELT data pipelines using SQL, Python, or PySpark. Perform data ingestion, transformation, validation, modeling, quality checks, documentation, monitoring, and incident support. Apply security, privacy, lineage, retention, and regulatory controls to sensitive insurance data. Collaborate with engineers, analysts, application teams, security, and governance stakeholders in a multinational agile environment.

Build and operate scalable data pipelines for autonomous-driving machine learning workflows. Responsibilities include ingesting, transforming, curating, validating, and monitoring large real-world and synthetic datasets; improving pipeline performance and reliability; supporting scenario mining and model evaluation; and collaborating with ML engineers, Data Corpus teams, AI Platform, and external partners.

Technical sales role supporting Japanese education customers with data, analytics, database, governance, and AI solutions. Responsibilities include designing architectures, delivering demos and proofs of concept, leading workshops and hackathons, developing modernization roadmaps, resolving technical blockers, and supporting migrations to Microsoft Cloud. The role collaborates with sales, services, partners, product, and engineering teams to expand Azure Data and Data & AI adoption across education, research, and operations.

Investigate and resolve high-risk identity, access, and security incidents involving compromise, lockouts, ownership validation, and access restoration. Troubleshoot complex multi-component systems using logs, traces, telemetry, and diagnostic tools; coordinate with engineering, product, and global support teams; participate in incident response and follow-the-sun coverage; communicate with customers; mentor engineers; and improve documentation, automation, workflows, and operational readiness.

Designs and maintains database solutions, ETL workflows, data pipelines, and cloud data integrations using PL/SQL, Python, and AWS. Provides application and production support, including incident resolution, monitoring, troubleshooting, root cause analysis, deployment, and release management. Owns change requests from requirements through post-production support, develops automation and applications, and collaborates with stakeholders to deliver scalable technical solutions.

Build full-stack tools, backend services, APIs, dashboards, and data workflows for assessing and improving AI training data and evaluations. Partner with quality and research teams to inspect agent trajectories, validate graders, measure quality, investigate workflow issues, and turn internal prototypes into reliable production platform capabilities.

Develop and maintain scalable Python applications, APIs, microservices, and data pipelines using PySpark and Airflow. Deploy and operate solutions on Azure with Docker and Kubernetes, implementing monitoring, logging, alerting, and production support. Collaborate with product managers, data scientists, and engineering teams on global projects while contributing to coding standards, architecture, testing, documentation, and Agile delivery.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Leads the design, development, deployment, and continuous improvement of scalable Azure data solutions. Builds cloud-native platforms using Spark, Databricks, Python, and SQL; establishes CI/CD, testing, automation, and DevOps practices; resolves complex technical issues; collaborates with global stakeholders; and mentors engineers while contributing to platform architecture and roadmap decisions.

Develop and deploy data extractors, integrations, data models, ingestion pipelines, visualizations, and AI agents for industrial clients. Design solutions using Python, SQL, Spark, cloud platforms, Kafka, Docker, and Cognite Data Fusion. Collaborate with customers, SMEs, product teams, and industrial stakeholders to translate requirements into scalable architectures and product capabilities. Ensure data contextualization, security, CI/CD, monitoring, logging, operational support, and technical quality while advising customers and partners.

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

Lead the design and development of cloud-based data platforms, pipelines, ETL/ELT processes, data models, warehouses, and lakes using Azure and Databricks. Build integrations, optimize performance, implement governance, monitor pipelines, resolve issues, and create Power BI reports. Collaborate with product managers and technical teams to deliver scalable, reusable data solutions and document processes.

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.

Lead the design and development of cloud-based data platforms using Azure and Databricks. Build and optimize ETL/ELT pipelines, data models, warehouses, lakes, and integrations; implement governance, monitoring, and troubleshooting; and create Power BI reports. Collaborate with product managers and cross-functional teams to deliver scalable, high-quality data solutions, while documenting processes and applying coding and DevOps best practices.

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.

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, 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.

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.

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 HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research 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.

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.