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日本
Onsite
Our Mission:
Driving technology always feels old. Not by a little bit. We believe vehicles can be a thousand times smarter, safer, and more connected to the world around us, and our mission is to see it happen. In 2019, we joined forces with Honda as their first startup acquisition, and now we’re expanding our vision into building the future of connected vehicles for millions of people around the world.
Why Drivemode:
Join Drivemode for an exciting startup environment and a vibrant culture that combines impactful work, competitive compensation, and excellent benefits. By becoming a part of our team, you'll contribute to a crucial mission that revolutionizes the way people engage with vehicles, addressing both business needs and the world's environmental challenges. This presents an exceptional opportunity to be at the forefront of innovation and drive Honda's success in the automotive market.
About The Role:
Drivemode is looking for a Senior Data Analyst to turn vehicle telemetry and user behavior data into reliable insights that guide product improvement. This is a hands-on individual contributor role within Engineering, working closely with the Analytics Product Manager and Product and Engineering teams.
You’ll work from a centrally governed, pseudonymized, preprocessed data layer (silver layer) in Snowflake, building the intermediate and analysis-ready models, dashboards, and analyses the company relies on. You’ll help translate business questions into well-defined problems and recommendations, especially around user behavior and UX improvements.
Beyond delivering analyses, you’ll help establish a data-driven culture by proactively identifying opportunities, improving data quality and analytical capabilities, and working across organizational boundaries to turn insights into measurable product improvements.
Work with the Analytics Product Manager and stakeholders to frame business questions, assess what the available data can answer, and deliver analyses and recommendations that inform product decisions.
Build and maintain intermediate data models shared across analysis-ready tables, along with the analysis-ready models and reference dashboards used by the company.
Create and maintain data quality checks, documentation, and a data catalog so others can find and use data independently.
Bring an informed perspective to prioritization by highlighting how data availability, quality, and effort affect what can be answered and when.
Own the quality and maintainability of the models and analyses you build, and collaborate with internal teams and Honda to improve data quality and analytical capabilities.
Use statistical reasoning and experimental-design principles to distinguish meaningful effects from noise, overfitting, and chance findings.
Proactively identify analytical opportunities and communicate findings clearly to support measurable product improvements and data-driven decisions.
5+ years in data analysis or product analytics, ideally in organizations with mature data practices and tooling.
Experience working with high-volume, evolving, and imperfect datasets, including the ability to investigate data quality and tracking issues.
Ability to frame problems around the underlying need, define hypotheses and metrics, and turn analysis into actionable recommendations.
A solid foundation in statistics and experimental design, including causal inference and distinguishing real effects from noise, overfitting, and chance findings.
Advanced SQL; proficiency in Python or R; and hands-on analytical data modeling (dimensional/Kimball, One Big Table, or other approaches) with dbt or an equivalent framework.
Product analytics experience with event-based tools such as Amplitude or Mixpanel, strong BI skills (Superset, Tableau, Power BI, or similar), and clear communication with technical and non-technical stakeholders.
A proactive, structured approach to data initiatives and stakeholder alignment, plus comfort with engineering workflows such as git, code review, and continuous delivery. Japanese proficiency sufficient for professional discussions with external stakeholders.
Experience with dbt, Snowflake, or Superset specifically.
Familiarity with orchestration tools (Airflow, Prefect, dbt Cloud) or data catalogs, and basic AWS knowledge (S3).
Automotive, telematics, or IoT domain experience.
Experience working with Automotive, Japan-based business teams, or globally distributed stakeholders.
