Senior Data Analyst

• 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.