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 financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Build and operate scalable financial market data infrastructure, including source evaluation, ingestion, cleansing, modeling, real-time and batch processing, quality governance, reconciliation, monitoring, and data services. Develop Flink-based pipelines supporting trading, research, and AI applications. Partner with product, trading, compliance, procurement, and engineering teams to define data contracts, source strategies, and consistent financial data semantics across markets.

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 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.

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, 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.

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.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

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.

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.

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.

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.