Senior Data Engineer, Behavior ML Planning & Prediction
Summary
Senior Data Engineer at Woven by Toyota (Toyota's mobility arm) building the data foundation for ML-based motion planning in autonomous driving. The role owns end-to-end data architecture and pipelines (ingestion to visualization) using SQL, Python, cloud warehouses like BigQuery/Snowflake, and tooling like dbt and Airflow, in a hybrid Tokyo setup.
TEAM
WHO ARE WE LOOKING FOR?
RESPONSIBILITIES
- Own and set the technical direction for the end-to-end data architecture that unifies our data into a coherent, discoverable, and reliable platform — evaluating design and operational trade-offs across scalability, reliability, and cost with a long-term view rather than optimizing locally
- Design and build data pipelines from ingestion through transformation to serving and visualization — sourcing, modeling, and delivering the canonical datasets that turn raw fleet and simulation logs into trusted, reusable 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 and objectively, influence roadmaps, and drive consensus toward a single, trusted data foundation — representing key insights clearly for both technical and non-technical audiences
- Define and own data products, Service Level Agreements, and the self-serve dashboards and tooling that scale analytics across the organization, along with the 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 that designs, trade-offs, and the resulting datasets are easy for others across the organization 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
MINIMUM QUALIFICATIONS
- 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), and 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 (e.g. 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
- A track record of technical leadership across teams — setting engineering standards that others adopt, aligning peers who have competing priorities or differing technical choices, and driving org-wide decisions to closure without formal authority, while growing the capability of other engineers
- Comfort operating in ambiguity — taking a loosely-defined, cross-team problem and creating the clarity, structure, and momentum to solve it
- Excellent communication skills in English, with the ability to explain complex technical trade-offs clearly and persuasively
NICE TO HAVES
- 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 — kinematics and dynamics, 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 for cross-functional users
- Business-level proficiency in Japanese