About HighLevel:
HighLevel is an AI powered, all-in-one white-label sales & marketing platform that empowers agencies, entrepreneurs, and businesses to elevate their digital presence and drive growth. We are proud to support a global and growing community of over 2 million businesses, comprised of agencies, consultants, and businesses of all sizes and industries. HighLevel empowers users with all the tools needed to capture, nurture, and close new leads into repeat customers. As of mid 2025, HighLevel processes over 4 billion API hits and handles more than 2.5 billion message events every day. Our platform manages over 470 terabytes of data distributed across five databases, operates with a network of over 250 microservices, and supports over 1 million hostnames.
Our People
With over 1,500 team members across 15+ countries, we operate in a global, remote-first environment. We are building more than software; we are building a global community rooted in creativity, collaboration, and impact. We take pride in cultivating a culture where innovation thrives, ideas are celebrated, and people come first, no matter where they call home.
Our Impact
As of mid 2025, our platform powers over 1.5 billion messages, helps generate over 200 million leads, and facilitates over 20 million conversations for the more than 2 million businesses we serve each month. Behind those numbers are real people growing their companies, connecting with customers, and making their mark - and we get to help make that happen.
About the Role:
We’re looking for a Staff Analytics Engineer to lead the definition, modeling, and governance of our enterprise data layer, which serves as the technical foundation that supports internal KPIs and investor reporting. This role owns the end-to-end technical standards for how data is modeled, tested, documented, and exposed across the company, ensuring that every number reported internally or externally is built on a consistent, auditable foundation. You’ll work at the intersection of data modeling, software engineering, and architecture, shaping the technical systems and conventions that keep our data accurate, governed, and verifiable from raw inputs through the datasets that support audits and disclosures.