Zeta Global - Staff Data Engineer
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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.
Responsibilities
• 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 common query patterns. • Build observability into the semantic layer with comprehensive metrics, logging, alerting, and query performance profiling. • Technical Leadership & Collaboration • 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.
Benefits
• Excellent medical, dental, and vision coverage • Employee Equity • Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!! • The salary range for this role is $170,000 - $200,000, depending on location and experience. • PEOPLE & CULTURE AT ZETA • Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression. • We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/ • ZETA IN THE NEWS! • ZETA IN THE NEWS! • https://zetaglobal.com/press/?cat=press-releases
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