Lead Analytics Engineer – Enterprise Data & AI

Zoox is seeking a high-agency, hands-on Lead Analytics Engineer to bridge the gap between our complex enterprise data sources and our AI-driven decision-making systems. You will lead the design and implementation of our semantic layer and data modeling strategy, ensuring that data from SAP (S/4HANA, Ariba, BRIM, ME), Workday, Salesforce, and Anaplan is transformed into clean, performant, and 'AI-ready' datasets. This is a critical leadership role for a builder who wants to own the data foundation that powers our intelligent agents and business-wide analytical workflows.

Responsibilities

Design and maintain a unified semantic model that provides a 'single source of truth' for cross-functional stakeholders, AI Agents, Self Serve Analytics and Executive dashboards.; Collaborate with Data & AI Engineers to structure and optimize the data that allows to query enterprise knowledge with high accuracy and low latency.; Establish organizational standards for data modeling, version control, testing, and documentation to ensure high data quality and system maintainability.; Implement automated testing and observability frameworks that proactively identify data anomalies and 'self-heal' pipelines, ensuring our data is always reliable for downstream consumption.; Partner with cross-functional business leaders to translate complex operational requirements into high-impact, scalable data solutions.

Requirements

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.; 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.; Exceptional ability to design modular, scalable, and performant data architectures that prioritize ease of use for downstream AI agents and Analytics tools.; A track record of driving technical projects from design to completion, mentoring junior engineers, and fostering a culture of collaboration and data excellence.

Nice to have

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.