Lead Analytics Engineer (Enterprise, Data & AI)

Join Zoox, a cutting-edge robotics company, as a Lead Analytics Engineer. In this critical leadership role, you will design and implement our semantic layer and data modeling strategy, ensuring that data from various enterprise sources is transformed into clean, performant, and "AI-ready" datasets. You will collaborate with cross-functional business leaders to translate complex operational requirements into high-impact, scalable data solutions. Enjoy a range of benefits, including paid parental leave, health insurance, and a comprehensive wellbeing program.

• Lead the design and implementation of the semantic layer and data modeling strategy, ensuring that data from various enterprise sources is transformed into clean, performant, and "AI-ready" datasets.
• Establish organizational standards for data modeling, version control, testing, and documentation to ensure high data quality and system maintainability.
• Collaborate with cross-functional business leaders to translate complex operational requirements into high-impact, scalable data solutions.
• You do not need to match every listed expectation to apply for this position
• Exceptional ability to design modular, scalable, and performant data architectures that prioritize ease of use for downstream AI agents and Analytics tools
• Deep understanding of data structures within large-scale enterprise platforms (SAP S/4HANA, Salesforce, Workday, etc.) and the ability to reconcile disparate schemas into clean models
• 10+ years in Data Engineering & Analytics, with extensive hands-on experience building a semantic framework using Python, SQL and modern orchestration frameworks (e.g. Airflow, Lakeflow, Argo). With at least 2+ years of hands-on experience deploying AI generated code
• Extensive experience using modern data stacks (e.g. Snowflake/Databricks, Big Query) to build complex, enterprise-grade data models
• A track record of driving technical projects from design to completion, mentoring junior engineers, and fostering a culture of collaboration and data excellence
• Experience using LLMs to automate data reconciliation, anomaly detection or root-cause analysis within analytics pipelines with cloud-native data platforms (e.g., Snowflake, Databricks)
• Expert-level Python & SQL skills with a focus on query optimization and performance tuning for massive datasets and reviewing AI generated code
• Proficiency in creating self-service Analytics environments (e.g., Tableau, Streamlit) that provide actionable insights to business stakeholders