Data Engineer - Enterprise Data & AI

Zoox

United States

1 day ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

8 years minimum

Working hours

Regular working hours

Job source

startup.jobs

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Airflow BigQuery Data Architecture Information Engineering Extract Transform Load (ETL) Data Synchronization Cloud Services Anaplan Salesforce.Com SAP (Applications)

+13 more

Data Processing Large Language Models Snowflake Kubernetes Data Analytics SAP S/4HANA SAP Ariba Terraform Data Pipelines Serverless Computing Workday SAP BRIM (Billing and Revenue Innovation Management) Databricks

Job description

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., * 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