Senior Data Engineer (Enterprise, Data & AI)

Join Zoox, a leading autonomous vehicle company, as a Senior Data Engineer. In this highly technical role, you will be responsible for building the framework to integrate complex enterprise sources into a unified data fabric. You will design and deploy self-healing data ingestion pipelines, build robust integration layers, and develop comprehensive telemetry and automated remediation strategies. You will also modernize our data architecture to handle high-volume, cross-functional data synchronization while maintaining strict security, compliance, and governance standards.

• Design and deploy self-healing data ingestion pipelines that automatically detect anomalies, perform schema evolution, and recover from failures without manual intervention.
• Build robust integration layers for diverse ecosystems, specifically focusing on SAP (S/4HANA, Ariba, BRIM, ME), Workday, Lever, Anaplan and Salesforce CRM.
• Develop comprehensive telemetry and automated remediation strategies to monitor data quality, latency and pipeline health in near real time.
• You do not need to match every listed expectation to apply for this position
• Proven ability to work with large-scale enterprise platforms (SAP S/4HANA, Salesforce, Workday, etc.) and understanding the nuances of their respective APIs and data models
• A "builder" mentality with a track record of driving complex infrastructure projects from architecture to production in fast-paced, high-stakes environments. Collaborating with cross-functional teams, AI & Analytics engineers
• Ability to design scalable, modular architectures that abstract the complexity of disparate enterprise systems into clean, usable data models
• Demonstrated experience in building "self-healing" systems, implementing circuit breakers, automated retry logic and robust error-handling & monitoring mechanisms
• 8+ years in Data Engineering, with extensive hands-on experience building production-grade ETL/ELT pipelines using Python, SQL and modern orchestration frameworks (e.g. Airflow, Lakeflow, Argo)
• Experience using LLMs to automate data reconciliation, anomaly detection or root-cause analysis within data pipelines
• Familiarity with cloud-native data platforms (e.g., Snowflake, BigQuery, Databricks)
• Familiarity with Terraform, Kubernetes or serverless compute to deploy and manage elastic, resilient data processing infrastructure
• Experience with Databricks Serverless, Managed tables, Zerobus and Variant