At ***, we're building the future of autonomous transportation, and data is at the heart of our mission. As a Fleet Operations Business Intelligence Engineer, you will work alongside Fleet Operations teams to analyze data and foster a culture of analytical rigor in support of data-driven operational decisions. You will be responsible for analyzing data, evaluating scenarios, setting up dashboards and new metrics, and providing actionable insights that will drive strategic decisions and improve fleet capacity and efficiency. Proficiency in SQL, Python, statistical analysis, and data visualization tools like Looker, Tableau, Databricks, and/or Power BI will be essential as you help transform raw data into meaningful insights that drive operational performance.
Key Responsibilities:
Data Processing & Cleanliness: Use SQL and Python to extract, clean, and process large datasets. Transform raw data into meaningful business metrics.
Data Analysis & Reporting: Analyze fleet performance and operational data, identify trends, and provide insights to improve fleet capacity and operational efficiency. Create and maintain interactive dashboards that track key performance indicators (KPIs) and operational metrics.
Visualization & Insights: Develop clear, concise visualizations and dashboards in Looker, Databricks, or equivalent tools to support operational decision-making. Ensure that these tools provide actionable insights for fleet operations and partner teams.
Collaboration with Fleet Operations Teams: Work closely with Fleet Operations teams to understand barriers to performance and identify areas for improvement using rigorous analytical methods.
Ad-Hoc Analysis: Complete ad-hoc analyses to help answer key business questions or evaluate the impact of potential changes to fleet operations. Identify trends in ad-hoc requests and propose business improvements.
Qualifications:
Education: Bachelor s or Master s degree in Data Science, Business Analytics, Statistics, Computer Science, Engineering, or a related field.
Experience:
3-5 years of experience in data analysis
Strong proficiency in SQL and data warehousing for data extraction, transformation, and modeling.
Proficiency with Python/R or equivalent for data cleaning, manipulation, and analysis.
Experience working with Data engineering, Product, Tech, and Business teams to map data availability, set up pipelines, and create visibility into operational data.
Experience creating and maintaining dashboards with tools like Looker, Tableau, Power BI, or similar visualization tools.
Skills & Competencies:
Strong analytical mindset with a passion for problem-solving and driving operational improvements.
Excellent communication skills, with the ability to explain technical concepts to a diverse, non-technical audience.
Ability to translate complex data into clear, actionable insights for a non-technical audience.
Ability to translate data requirements into clear deliverables across multiple interdependent teams.
Bonus Qualifications:
Familiarity with machine learning or predictive analytics techniques to anticipate operational needs.
Experience with workforce management, labor planning, or cost optimization analysis.
Attention to detail and a commitment to ensuring data accuracy.