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ZooxZooxFoster City, CA

Senior Data Engineer

Build self-healing data ingestion pipelines and integration layers for enterprise systems (SAP, Salesforce, Workday) to deliver AI-ready data for autonomous agents and analytics. Requires 8+ years building production ETL/ELT pipelines with Python, SQL, and orchestration tools.

206k – 247k
Hybrid8+ YOEData Engineering

About the role

Responsibilities

  • 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.

Requirements

  • 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.

Nice-to-Haves

  • 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

Skills

PythonSQLAirflowLakeflowArgoSap S/4HanaSalesforceWorkdaySnowflakeBigQueryDatabricksTerraformKubernetesLLMs

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