Lead Analytics Engineer (Enterprise Data & AI)

Location: Hybrid (Foster City, CA). Must be based in or able to work from Foster City, CA

Salary: $236,000 - $284,000 per year

Company

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem for urban mobility-as-a-service.

What you will do

• Design and maintain a unified semantic model as a single source of truth for cross-functional stakeholders, AI agents, and executive dashboards.
• Collaborate with Data & AI Engineers to structure and optimize data for high-accuracy, low-latency queries.
• Establish organizational standards for data modeling, version control, testing, and documentation.
• Implement automated testing and observability frameworks to proactively identify and heal data pipeline anomalies.
• Partner with business leaders to translate complex operational requirements into scalable data solutions.

Requirements

• 10+ years in Data Engineering & Analytics with Python, SQL, and orchestration frameworks (Airflow, Lakeflow, Argo).
• At least 2+ years of hands-on experience deploying AI-generated code.
• Extensive experience with modern data stacks such as Snowflake, Databricks, or BigQuery.
• Deep understanding of data structures within large-scale enterprise platforms including SAP S/4HANA, Salesforce, and Workday.
• Proven track record of driving technical projects to completion and mentoring junior engineers.

Nice to have

• Experience using LLMs to automate data reconciliation, anomaly detection, or root-cause analysis.
• Expert-level Python & SQL skills with a focus on query optimization for massive datasets.
• Proficiency in creating self-service analytics environments using Tableau or Streamlit.

Culture & Benefits

• Competitive compensation including base salary, Amazon RSUs, and Zoox Stock Appreciation Rights.
• Comprehensive health, long-term care, and life insurance.
• Paid time off (sick leave, vacation, bereavement) and unpaid time off.
• A culture that values diverse perspectives and focuses on high-execution in a fast-moving environment.