Staff Data Engineer

Zeta Global is an AI-Powered Marketing Cloud company that focuses on leveraging advanced artificial intelligence to enhance customer acquisition and retention for marketers. They are seeking a Staff Data Engineer to lead the design and implementation of a unified semantic data layer that integrates various data sources, enabling high-performance data interaction for AI systems.

Responsibilities:

• 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
• 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)
• 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
• 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
• 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 common query patterns
• Build observability into the semantic layer with comprehensive metrics, logging, alerting, and query performance profiling
• Serve as the technical authority on data architecture decisions, authoring ADRs (Architecture Decision Records) and reference architectures
• Mentor and guide senior engineers on best practices for semantic modeling, data governance, and API design
• Partner cross-functionally with Product, Data Science, Platform Engineering, InfoSec, and Compliance teams to align the data layer with business objectives

Requirements:

• 10+ years of experience in data engineering, data architecture, or platform engineering, with at least 3 years operating at a Staff/Principal level
• Deep hands-on expertise with multiple data stores: relational (MySQL/PostgreSQL), NoSQL (DynamoDB, Aerospike, MongoDB), cloud data warehouses (Snowflake, BigQuery, Redshift), and data lakes (S3, Delta Lake, Iceberg)
• Strong experience with streaming and messaging systems: Apache Kafka, Amazon SQS/SNS, Kinesis, or equivalent
• Proven experience building or operating semantic/metrics layers using Cube.js/Cube Core, dbt Metrics, LookML, or similar technologies
• Expert-level SQL skills and experience with query optimization across distributed systems
• Production experience designing multi-tenant data platforms with strict security and isolation requirements
• Strong understanding of data governance, access control models (RBAC, ABAC), and compliance frameworks (SOC 2, GDPR, CCPA)
• Experience designing and exposing APIs (REST, GraphQL) for data consumption at scale
• BS/MS in Computer Science, Data Engineering, or equivalent practical experience
• Experience building data interfaces specifically for AI/ML consumption, including tool-use APIs for LLM agents, MCP (Model Context Protocol), or function-calling patterns
• Familiarity with AI orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel) and how they interact with external data tools
• Experience with infrastructure-as-code (Terraform, Pulumi), container orchestration (Kubernetes, ECS), and CI/CD pipelines for data platform deployments
• Background in MarTech/AdTech data domains: identity graphs, audience segmentation, campaign analytics, attribution modeling, or real-time bidding data
• Contributions to open-source data tools or published thought leadership on semantic layers, data mesh, or AI-enabled data architectures