Zoox

Fully electric autonomous ride-hailing service platform

Lead Data Platform Engineer - Enterprise, Data & AI

Data Management +1Confirmed live in the last 24 hours

Full-timeFoster City, CA, USA$230k - $277k /yr

About the Job

Requirements

At least 10 years of hands-on experience in Data Platform Engineering or Software Engineering, including a track record of architecting and scaling production-grade data foundations.
Expertise in scaling and optimization, query tuning, compute resource allocation, and efficient compute-storage lifecycle policies to minimize infrastructure costs.
Experience implementing enterprise security standards, role-based access control, Active Directory or IAM roles, and fine-grained data masking, with familiarity supporting enterprise compliance requirements such as SOX, audit trail controls, and data retention policies.
A track record of establishing enterprise data governance frameworks, automated schema evolution controls, data quality audits, and near real-time observability and alerting.
Hands-on experience building automated CI/CD pipelines, environment isolation such as branch testing and rollback mechanisms, version control, and automated deployment testing for data assets.

Responsibilities

Scale 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 versus EMR (AWS) cloud cost and performance optimization.
Build automated CI/CD deployment templates, environment isolation, testing frameworks, and version control standards to enable rapid, reliable, zero-downtime deployments.

Desired Qualifications

Experience incorporating LLMs or generative AI directly into pipeline operations, such as code generation, triage and root-cause analysis, automated data reconciliation, backfills, or anomaly detection.
Experience with infrastructure-as-code using Terraform and containerization or orchestration with EKS for elastic compute management, including GPU/CPU performance optimization and utilization.
Previous experience in the autonomous vehicle, robotics, or high-tech manufacturing sectors.

Salary Range: 230000 to 277000 (Currency: USD) (Pay period: per-year-salary)

In This Role, You Will...

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

Qualifications

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.

Bonus Qualifications

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.

Previous experience in the autonomous vehicle, robotics or high-tech manufacturing sectors.

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.

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to [email protected] 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.

About the Company

Zoox

Fully electric autonomous ride-hailing service platform

Zoox designs and operates an autonomous ride-hailing service built around a purpose-built, fully electric vehicle designed specifically for autonomous operation. The vehicle uses advanced sensors, cameras, and AI to navigate complex urban environments, offering a four-passenger interior for comfort. Unlike competitors that retrofit existing cars, Zoox engineers both the vehicle and the service from the ground up, enabling optimized safety, efficiency, and passenger experience through integrated software that optimizes routing and reduces wait times. The company emphasizes safety through extensive testing and regulatory collaboration, and sustainability via electric mobility. The ultimate goal is to provide a smarter, safer, and more efficient urban transportation system that improves city life while reducing emissions.

Company size: 5,001-10,000
Company stage: Acquired
Total funding: $1.2B
Headquarters: Menlo Park, CA
Founded: 2014

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