WHO WE ARE
Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.
THE ROLE
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As a Senior Data Platform Engineer, you will build and operate the data infrastructure that supports Zeta’s products, analytics, machine learning, and AI systems.
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This is a broad platform role. Engineers on the team take ownership across Lakehouse architecture, distributed compute, data quality, governance, lineage, security, access controls, and the operational systems that keep the platform running. Individual focus areas will change with team priorities, and you will contribute across the platform as needed.
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The team also develops tooling for type-safe, testable data pipelines and operates Spark workloads on dynamically sized Hadoop clusters backed by EC2 Spot instances. We work closely with data engineers, data scientists, infrastructure teams, and product teams to make data dependable, easy to find, secure to use, and cost-effective to process.
This is a hybrid role based out of either our San Mateo, CA or San Francisco, CA office.
RESPONSIBILITIES
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Build and operate Lakehouse, Spark, and distributed data-processing systems.
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Improve the performance, reliability, cost, and operational visibility of platform workloads.
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Develop capabilities for data quality, governance, lineage, metadata, discoverability, security, and access control.
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Build the data context required for trusted reporting, ML models, and AI agents.
