Paid Operations Engineer (AI + Automation) (m/f/d)

Imagine you ship an automation in a few days that removes a recurring paid-ops pain (budget pacing checks, QA, naming hygiene, tracking validation). Two weeks later it runs reliably across accounts, saves the team hours every week, and improves data quality and performance at the same time.

At YOYABA we’re building leverage through operations, automation, and AI-first workflows - so paid media doesn’t just get “managed,” it becomes systematically scalable. This isn’t a classic performance marketer role and not a pure data role either. It’s ops + engineering with ownership, speed, and measurable impact.

The role

We’re looking for a Paid Operations Engineer who understands the realities of paid platforms, can set up and maintain clean marketing/measurement systems, and builds automations + data flows that hold up in production.

Two focus areas:

• Automation & AI-first Engineering: workflows, integrations, scripts, and small tools that solve paid ops problems permanently (not just once).

• Paid & Marketing Operations: Collaborate with the performance Marketing team on governance, QA, tracking/attribution hygiene, pacing, reporting aimed at fewer manual steps, fewer mistakes, better decisions.

You won’t get a ticket list. You’ll get goals, context, and ownership: find → build → measure → iterate.

What you’ll do

Build automations, integrations & lightweight tools

• Automate repetitive paid/marops workflows (reporting, alerts, QA, pacing, feed/asset checks, campaign setup, Ad Manager activities, etc.)

• Build on top of existing infrastructure (BigQuery, Postgres, G-Cloud, n8n).

• Use AI (coding) tools as your acceleration layer (e.g., n8n, Claude Code, Cursor) to prototype, refactor, test, and document faster.

• Keep it production-minded: structured outputs, quality controls, tool/function calling, security, so the solutions stay reliable.

• Build robust pipelines: pull → normalize → validate → surface (dashboards, alerts, internal views).

• Turn tribal knowledge into intelligent systems: clear logic, instructions, iterations.

Co-operate with the Performance Marketing Team on Operations

• Co-own standards for Campaign and asset execution (processes).

• Support with tracking setups and debug tracking/attribution pragmatically: isolate root causes fast, fix cleanly, prevent regressions.

• Build views that make Ops decisions easier and iterative (pacing, funnel, CAC/ROAS, data quality).

Tech environment

• Paid platforms: Mainly Google Ads, Meta Ads, LinkedIn Ads, (+ Bing Ads, Reddit Ads, etc.)

• Tracking & measurement support: Google Tag Manager, Google Analytics 4, conversion APIs

• Data stack: Windsor, BigQuery, postgres, g-cloud

• Automation tooling: n8n, g-cloud

• Collaboration: Asana, Google Workspace, Slack, Figma

What success looks like (first 3 - 6 months)

• Noticeably less manual work, higher data quality, and smoother scaling across accounts/projects.

• Integration of your solutions in larger tech stack & daily work of paid team

• 3 - 6 automations/tools that demonstrably save time or prevent errors.

• An upgraded ops/performance dashboarding (dashboards + alerts) that helps drive decisions.