Senior Data Engineer – Enterprise Data & AI

Zoox is seeking a highly motivated, hands-on Data Engineer to build the next generation of autonomous, self-healing data pipelines. You will be the primary responsible for building the framework to integrate complex, mission-critical enterprise sources including SAP (S/4HANA, Ariba, BRIM, ME) across Procurement, Supply Chain, Legal, Finance, HR and Marketing into a unified data fabric. This is a highly technical role for an engineer who thrives on building resilient, automated systems that ensure high-fidelity data is always available for our AI agents and Analytics workflows.

In this role, you will:

• 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.
• Ensure that data is cleaned, structured, and served in an "AI-ready" format, enabling our AI agents to query and interact with enterprise data reliably.
• Modernize our data architecture to handle high-volume, cross-functional data synchronization while maintaining strict security, compliance, and governance standards.

Qualifications:

• 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).
• 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.
• Demonstrated experience in building "self-healing" systems, implementing circuit breakers, automated retry logic and robust error-handling & monitoring mechanisms.
• Ability to design scalable, modular architectures that abstract the complexity of disparate enterprise systems into clean, usable 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.

Bonus Qualifications:

• 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