Staff Data Engineer

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 Opportunity We are looking for a Staff Data Engineer to lead the design and implementation of a unified semantic data layer that spans all of Zeta’s data sources—both data at rest and data in motion. This role sits at the intersection of data engineering, platform architecture, and AI enablement. You will be responsible for building a middleware semantic layer (using Cube Core or similar technologies) that exposes clean, governed, multi-tenant data via standardized APIs and tool interfaces, enabling AI agents and LLMs to query, reason over, and act on Zeta’s data with high performance, security, and compliance. This is a high-impact, high-visibility role that will shape how Zeta’s AI systems consume and interact with data across the organization. What You’ll Do Semantic Layer Architecture Development Design and build a centralized semantic data layer using Cube Core (or equivalent technology such as Headless BI, dbt Metrics Layer, or Metriql) that provides a unified, governed abstraction over all company data sources. Define semantic models, metrics, dimensions, and relationships that map to business domains across marketing, advertising, identity resolution, and customer analytics. Expose the semantic layer via REST/GraphQL APIs and MCP-compatible tool interfaces purpose-built for consumption by AI agents and LLMs. Data Source Integration Unification Integrate and unify data from heterogeneous systems including MySQL, DynamoDB, Aerospike, Snowflake, Amazon S3 (data lakes), Apache Kafka, Amazon SQS, and other internal data stores. Build connectors, adapters, and federation layers to query across both operational (OLTP) and analytical (OLAP) data sources in a performant, cost-efficient manner. Ensure seamless handling of both data at rest (warehouses, lakes, databases) and data in motion (streaming platforms, event buses, message queues). AI LLM Enablement Design tool interfaces and API contracts that allow AI agents to discover available data, understand schema semantics, and generate accurate queries autonomously. Collaborate with AI/ML teams to optimize the semantic layer for LLM-generated SQL, natural language querying, retrieval-augmented generation (RAG), and agentic workflows. Implement guardrails, query validation, and cost controls to prevent runaway queries from AI-generated workloads. Multi-Tenancy, Security Compliance Architect the semantic layer with native multi-tenant isolation, ensuring strict data segregation and tenant-scoped access controls. Implement row-level security, column-level masking, and attribute-based access controls (ABAC) to enforce data governance policies. Ensure compliance with SOC 2, GDPR, CCPA, and industry-specific regulations governing data access, PII handling, and cross-border data flows. Performance, Scalability Reliability Design for horizontal scalability to support thousands of concurrent queries from AI agents, internal dashboards, and customer-facing products. Implement intelligent caching (pre-aggregation, materialized views, query result caching) to deliver sub-second response times for c ... (truncated, view full listing at source)