Data Automation Engineer

Zeta Global•New York City, NY

11h•Remote

About The Position

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.

Nice To Haves

Think pragmatically. You default to the simplest solution that works and scales.
Prototype quickly. You’d rather ship a working automation this week than debate frameworks for a month.
Care about outcomes. An automation that isn’t used or doesn’t deliver value isn’t “done.”
Translate easily. You can talk ROI with stakeholders and implementation details with engineers.
Love the blank canvas. Ambiguity is fuel, not friction.
Can cut through complex problems.

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.