Quality Assurance Analyst

We're looking for a Quality Assurance Analyst (Paid Ads) to ensure every ad campaign that goes live meets performance and quality standards.

If you're meticulous with data, understand ad platform metrics, and know how to spot inefficiencies in campaign setups, this role is for you. You'll review the work of Paid Ads Specialists, standardize QA procedures, and track key performance indicators that drive accountability and consistency.

This is a hands-on role where you'll help build the QA process from the ground up while contributing to the broader success of automated campaign workflows.

Why You'll Want to Join:

• You will be paid in USD (bi-monthly: every 15th and 30th)
• Up to 14 days of Paid Time Off annually (starting Day 1)
• Observance of Holidays (based on your location)
• 100% remote setup so you can work wherever you feel most productive
• Collaborate with a global marketing and product team
• Be part of a SaaS company redefining how automation improves ad and SEO performance

What You'll Work On:

Campaign QA and Review

• Review ad campaigns built by Paid Ads Specialists across Meta, Google, TikTok, and LinkedIn.
• Ensure correct targeting, tracking, creative placement, and adherence to naming conventions and budget parameters.
• Identify and document recurring errors or setup inconsistencies.

QA Process Development

• Design and implement a structured QA workflow for paid media deliverables.
• Create checklists and review templates to maintain quality standards.
• Build QA dashboards tracking key metrics such as campaign accuracy rate, error frequency, and correction turnaround time.

Reporting and Insights

• Deliver weekly QA reports summarizing key findings and trends.
• Prepare Monthly QA Summaries for the Operations Manager highlighting progress, recurring issues, and optimization opportunities.
• Collaborate with the Paid Ads and Operations teams to close feedback loops and improve campaign execution.

What You Bring:

• 2 to 4 years of experience in paid media operations, campaign management, or ad quality assurance
• Strong working knowledge of Meta Ads, Google Ads, or TikTok Ads Manager
• Understanding of ad structure, audience targeting, and tracking setups
• Experience creating QA dashboards or reports in Google Sheets, Data Studio (Looker), or Airtable
• High attention to detail and the ability to manage reviews across multiple campaigns simultaneously
• Comfortable working in a remote, fast-moving, startup environment

Nice to Have

• Familiarity with automation tools or ad management platforms
• Experience working with AI-powered campaign builders or SaaS products
• Ability to analyze performance data and make actionable recommendations

How to Apply:

Please include:

• Your updated resume highlighting paid ads or QA experience
• A short 1–2 minute Loom video introducing yourself and describing how you ensure campaign quality and accuracy

Only candidates who submit a Loom video will move forward.

If you enjoy finding gaps, improving systems, and ensuring campaigns run exactly as intended, this role will let you make a measurable impact on performance and process.

Application Process Overview

Our comprehensive selection process ensures we find the right fit for both you and our clients:

• Initial Application - Submit your application and complete our prequalifying questions
• Video Introduction - Record an video introduction to showcase your communication skills and work experience
• Role-Specific Assessment - Complete a homework assignment tailored to the position (if applicable)
• Recruitment Interview - Initial screening with our talent team
• Executive Interview - Meet with senior leadership to discuss role alignment
• Client Interview - Final interview with the client team you'd be supporting
• Job Offer - Successful candidates receive a formal offer to join the team

Each stage is designed to evaluate your fit for the role while giving you insights into our company culture and expectations. We'll keep you informed throughout the process and provide feedback at each step.