Top Data Engineer Jobs in Yokohama

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.

Build and maintain batch and streaming data pipelines, Airflow workflows, data models, and warehouse datasets for a crypto trading platform. Improve data quality, freshness, performance, and cost efficiency through monitoring, validation, troubleshooting, and optimization. Collaborate with engineering and business users to define datasets, metrics, and documentation. This fully remote APAC role requires strong Python and SQL skills, hands-on Kafka, Spark, and Airflow experience, crypto or Web3 data engineering experience, and clear Mandarin communication.

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.

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.

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.

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.

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.

Own the Data Products function across reporting, analytics, immutable ledgers, shareholder intelligence, and compliance governance. Lead strategy, architecture, customer discovery, production development, data engineering, real-time reporting, APIs, AI-enabled querying, disaster recovery, and governance. Collaborate with product leadership on onboarding and ingestion while representing the function to customers, executives, board members, and prospects.

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.