Data Automation Engineer

Zeta Global

5h•$110,000 - $125,000•Remote

About The Position

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 We’re a lean crew of business-savvy technologists who prototype first and perfect later. Our charter is to uncover opportunity, launch Version 0.1 in days, measure real-world impact, then iterate without ego. We mix cutting-edge tools with brilliantly low-tech hacks—whatever accelerates the outcome. Automation is core to how we work. We’re looking for a builder who can spot repeatable pain, choose the right tools, wire systems together, and deliver automations that actually get used and create measurable value.

Requirements

Experience building automations across cloud platforms (AWS and/or Azure) and modern data platforms like Snowflake.
Strong hands-on development experience with Python and SQL, with some experience in another language such as JavaScript or C#
Experience scheduling and orchestrating jobs (e.g., serverless functions, Airflow-like tools, cron-based systems, event-driven workflows).
Proven examples of automations that reduced cost, saved time, improved reliability, or unlocked new capabilities.
Experience integrating APIs, internal services, and third-party tools.
Familiarity with AI-assisted automation (LLMs, classification, extraction, decision support), with a pragmatic approach to when not to use AI.
Comfortable working end-to-end: requirements - design – build - deploy - measure.

Responsibilities

Identify and prioritize automation opportunities: Partner with stakeholders to understand workflows, gather requirements, and continuously prioritize automation work based on impact (time saved, cost reduced, reliability improved).
Design end-to-end automation: Select the right tools and patterns - serverless jobs, schedulers, cloud-native services, scripts, or AI-assisted workflows - and build solutions from first trigger to final outcome.
Build across the modern data stack: Create and maintain automations spanning AWS, Azure, and Snowflake, integrating with APIs, data pipelines, and internal services.
Engineer production-ready jobs: Write clean, reliable automation code using Python, SQL, C#, and TypeScript/JavaScript while handling scheduling, retries, logging, alerting, and failure modes.
Leverage AI where it makes sense: Build automations both with and without AI integrations - using AI to accelerate classification, enrichment, validation, or decision-making when it delivers clear benefits.
Drive measurable outcomes: Ensure every automation delivers value: track usage, validate impact, tune performance, and iterate or retire workflows based on real results.
Hunt for efficiencies: Proactively spot manual steps, brittle processes, or expensive workflows and replace them with faster, cheaper, more reliable automation.
Raise the bar for everyone: Document patterns, share reusable components, and help teams think more systematically about automation, creating systems instead of one-off scripts.