Senior Data Engineer, Behavior ML Planning & Prediction

TEAM

The Behavior team at Woven by Toyota builds the data foundation for data-driven autonomy. Our work spans a variety of challenges — analyzing petabytes of multimodal fleet and simulation data, designing scalable data pipelines from ingestion to visualization, and delivering the canonical datasets, metrics, and self-serve tools that power ML-based motion planning across both onboard and offboard systems. We are looking for doers and creative problem solvers to join us in improving mobility for everyone with human-centered automated driving solutions.

WHO ARE WE LOOKING FOR?

The team is looking for an experienced Senior Data Engineer to work in close collaboration with our ML and platform teams to source, transform, and serve high-quality datasets — building data pipelines from ingestion to visualization, and providing the tools and introspection that let others build on our data with confidence. You will have the chance to design and own the data architecture behind multiple large-scale projects, unify data products into a single, trusted foundation, and help accelerate the release of models for our next-generation autonomous vehicle platform, influencing millions of Toyota customer vehicles. We are looking for individuals who are passionate about self-driving car technology and its potential impact on humanity.
As a senior member of the team, you will act as a technical leader — mentoring engineers, setting the bar for data engineering craft, and raising the data capability of the teams around you. We highly value a "Giver" mindset — consistently asking "What can I do for you?" — combined with the conviction to advocate for the right long-term design even when it is a harder conversation.
This is a role with significant scope and autonomy. You will set a technical direction that outlasts any single project, thrive on ambiguous, blank-slate problems where the right structure has yet to be defined, and grow your sphere of influence and technical leadership as our data platform matures. It is well suited to an exceptional senior engineer ready to operate at a broader, organization-wide altitude.
You appreciate a hybrid workspace and can come to our Nihonbashi (Tokyo) office three days per week.

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