About Manifest Global Manifest Global is building the infrastructure for global human capital mobility — connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale.

$80M raised. Still early. What This Role Is When a student in India opens Cialfo to research universities in the UK, Canada, or Australia, they are looking at data that someone collected, validated, and kept current.

University profiles. Course listings.

Requirements

Application deadlines. Tuition fees. Scholarship information.

Across 544 partner universities and thousands more, every piece of that data has to be accurate, up to date, and trustworthy enough to inform one of the most significant decisions a young person makes. Right now, a meaningful portion of that work is manual. The University Data Engineering team has the domain knowledge to know what correct university data looks like.

What it doesn't have is the technical capability to automate the work that shouldn't be manual. That's what this role brings. As Data Automation Engineer, you'll own the automation function end to end: building the scrapers, AI-powered workflows, and data pipelines that replace manual data collection with reliable, production-grade automation.

You'll report into Engineering and work directly alongside the University Data Engineering team as the sole owner of the technical stack. This is a building role. There is no existing automation function to inherit.

You are creating it. What Makes This Role Different Most data engineering roles involve maintaining pipelines someone else designed or contributing to a function where the architecture decisions are made above you. This one doesn't work that way.

You make the tooling decisions. N8N or Python. Firecrawl or Playwright.

Claude API or rule-based extraction. You explain your reasoning and you own the outcome. The University Data Engineering team will QC your output honestly.

When something is wrong, you'll hear it clearly. That's a feature, not a friction point: it's what makes the automations better. The work ships directly to a product used by hundreds of thousands of students making real university decisions.

When you build a notification classifier that saves the team 20 hours a week, or a quality audit agent that catches data errors before they reach students, the impact is immediate and visible. What You'll Build Your first 90 days have a defined backlog, in rough order of priority: A notification classification pipeline handling 450 alerts per week, replacing six hours of daily manual signal triage across the team. A signal addressal workflow processing 150 signals per week, replacing six hours of daily Core data updates.

An automated quality audit agent running nightly across all recent data updates, replacing six to seven hours of daily manual accuracy checks. A rankings and key stats ingestion system covering 4,441 universities, replacing the full manual collection cycle for QS, THE, and US News rankings.

automation for 150+ universities using dynamic, JavaScript-rendered pages that currently require manual extraction. Beyond the initial backlog, you own the full 25-task automation portfolio: maintaining what exists, rebuilding scrapers when source sites change structure, and designing new automations as the team's data commitments grow.