Overview
Scaling self-healing data pipelines that automatically handle schema evolution, detect anomalies, execute circuit breakers and recover from failures without manual intervention. Define and enforce company-wide data governance, access, automated data quality testing, schema evolution policies and metadata management to maintain high-fidelity data assets. Implement intelligent storage lifecycle strategies, resource throttling and query optimization to minimize compute overhead while delivering high-throughput, low-latency data access for analytics and AI agents. With a focus on Databricks vs EMR (AWS) cloud optimization (cost and performance). Build automated CI/CD deployment templates, environment isolation, testing frameworks and version control standards, enabling rapid, reliable and zero-downtime deployments
Full job description
In This Role, You Will...
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Scaling self-healing data pipelines that automatically handle schema evolution, detect anomalies, execute circuit breakers and recover from failures without manual intervention.
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Define and enforce company-wide data governance, access, automated data quality testing, schema evolution policies and metadata management to maintain high-fidelity data assets.
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Implement intelligent storage lifecycle strategies, resource throttling and query optimization to minimize compute overhead while delivering high-throughput, low-latency data access for analytics and AI agents. With a focus on Databricks vs EMR (AWS) cloud optimization (cost and performance).
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Build automated CI/CD deployment templates, environment isolation, testing frameworks and version control standards, enabling rapid, reliable and zero-downtime deployments
Qualifications And Requirements
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10+ years of hands-on experience in Data Platform Engineering or Software Engineering with a proven track record of architecting and scaling production-grade data foundations.
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Deep expertise in scaling and optimization, query tuning, compute resource allocation and implementing efficient compute-storage lifecycle policies to minimize infrastructure costs.
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Experience implementing enterprise security standards, Role-Based Access Control (RBAC), Active Directory/IAM roles and fine-grained data masking, with strong familiarity supporting enterprise compliance requirements (e.g., SOX, audit trail controls, data retention policies).
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Track record of establishing enterprise data governance frameworks, automated schema evolution controls, data quality audits and near real-time observability/alerting.
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Hands-on experience building automated CI/CD pipelines, environment isolation (branch testing, rollback mechanisms), version control and automated deployment testing for data assets.
Bonus Qualifications
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Experience incorporating LLMs or GenAI directly into pipeline operations (e.g., code generation, triage and root-cause analysis, automated data reconciliation, backfills or anomaly detection).
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Experience with infrastructure-as-code (Terraform) and containerization/orchestration (EKS) for elastic compute management. GPU/CPU performance optimization & utilization.
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Previous experience in the autonomous vehicle, robotics or high-tech manufacturing sectors.
Additional information
About Zoox Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
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Accommodations If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.
A Final Note: You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
Requirements & qualifications
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10+ years of hands-on experience in Data Platform Engineering or Software Engineering with a proven track record of architecting and scaling production-grade data foundations.
-
Deep expertise in scaling and optimization, query tuning, compute resource allocation and implementing efficient compute-storage lifecycle policies to minimize infrastructure costs.
-
Experience implementing enterprise security standards, Role-Based Access Control (RBAC), Active Directory/IAM roles and fine-grained data masking, with strong familiarity supporting enterprise compliance requirements (e.g., SOX, audit trail controls, data retention policies).
-
Track record of establishing enterprise data governance frameworks, automated schema evolution controls, data quality audits and near real-time observability/alerting.
-
Hands-on experience building automated CI/CD pipelines, environment isolation (branch testing, rollback mechanisms), version control and automated deployment testing for data assets.
-
Experience incorporating LLMs or GenAI directly into pipeline operations (e.g., code generation, triage and root-cause analysis, automated data reconciliation, backfills or anomaly detection).
-
Experience with infrastructure-as-code (Terraform) and containerization/orchestration (EKS) for elastic compute management. GPU/CPU performance optimization & utilization.
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Previous experience in the autonomous vehicle, robotics or high-tech manufacturing sectors.
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