Senior Data Engineer, Behavior ML Planning & Prediction

業務内容

• Own and set the technical direction for the end-to-end data architecture that unifies data into a coherent, discoverable, and reliable platform, evaluating design and trade-offs across scalability, reliability, and cost with a long-term view.
• Design and build data pipelines from ingestion through transformation to serving and visualization — sourcing, modeling, and delivering canonical datasets that turn raw fleet and simulation logs into trusted data, and keeping them consistent across teams.
• Set shared technical direction across teams: partner with stakeholders org-wide to understand their data needs, weigh technical trade-offs rigorously, influence roadmaps, and drive consensus toward a single, trusted data foundation; represent insights clearly for technical and non-technical audiences.
• Define and own data products, Service Level Agreements, and self-serve dashboards and tooling that scale analytics across the organization, along with monitoring, alerting, and operational practices that keep those promises.
• De-risk major architectural bets before the organization commits to them, using rapid prototypes and focused technical investigations to turn open questions into evidence-based decisions.
• Document architecture, data models, interfaces, and decisions clearly, so designs, trade-offs, and datasets are easy for others to understand, adopt, and maintain.
• Act as a technical leader beyond the team: mentor engineers, establish data engineering best practices and standards that other teams adopt, and raise the data capability of the wider Autonomy organization.

技術スタック

必須スキル

• 7+ years of experience building and operating production data pipelines and data platforms at scale
• Deep command of SQL and a modern programming language (e.g., Python); hands-on expertise designing robust data models and multi-step ETL/ELT jobs
• Experience with a cloud data warehouse (e.g., BigQuery, Snowflake, Redshift) and with orchestration and transformation tooling (dbt, Airflow, or equivalents)
• Demonstrated ownership of the data architecture for large-scale systems — setting technical direction and reasoning explicitly about scalability, reliability, security, and cost trade-offs
• Excellent communication skills in English, with the ability to explain complex technical trade-offs clearly and persuasively

歓迎スキル（該当する場合）

• Experience unifying or consolidating data across multiple pipelines, formats, or storage systems onto a common platform, or migrating from bespoke dataset formats to an open table format (e.g., Apache Iceberg) as the basis of a lakehouse architecture
• Experience establishing data products, contracts, and SLAs for widely-used datasets, along with data-quality frameworks and observability
• Experience with large-scale, multimodal data — including spatial and temporal/sequential data (e.g., sensor, log, simulation, time-series, trajectory, or scene/snapshot representations) — and modeling it for reliable downstream use
• Familiarity with autonomous driving or robotics domain concepts (e.g., vehicle motion, trajectories, coordinate frames; motion planning and prediction; perception; mapping and localization) and how they shape the data we work with
• Familiarity with distributed data processing (e.g., Spark, Ray), workflow orchestration (e.g., Flyte/Union, Airflow), and columnar/lakehouse formats (e.g., Parquet, Iceberg); experience building self-serve analytics products, semantic layers, or BI/dashboarding tooling
• Business-level proficiency in Japanese

キャリア成長観点

• Opportunity to shape the data architecture behind data-driven autonomous driving initiatives at Woven by Toyota, potentially influencing millions of Toyota customer vehicles.
• Senior technical leadership path: mentoring engineers, setting engineering standards, and driving cross-team data capability and alignment beyond individual projects.
• Exposure to end-to-end data product lifecycle (data contracts, SLAs, observability, self-serve analytics) and the chance to architect scalable data products used across the organization.
• Hybrid Tokyo-based role with collaboration across ML, platform, and autonomy teams, offering high-visibility impact and clear pathways to broader organizational influence.
• Deep engagement with multimodal, lakehouse-style data, data quality and observability practices, and cross-functional storytelling to translate complex trade-offs into actionable roadmaps.