Senior Analytics Engineer – Product & Customer Analytics

About HighLevel:
HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes.
To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently.
Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.
Our People
With over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.
Our Impact
Every month, HighLevel enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact. We’re proud to be a part of that.
Learn more about us on our YouTube Channel or Blog Posts

About the Role:

We are looking for an Senior Analytics Engineer to support the Customer Data Platform by building production-grade models and datasets that power in-app analytics surfaces. This is a hands-on execution role focused on correctness, maintainability, and consistent reuse.
You will work within modeling standards, event contracts, and definitions set by the CDP team, and partner with product engineering teams as they onboard onto standard analytics patterns.

Responsibilities:

• Develop and maintain dbt models that transform event and customer data into reusable dimensions and facts
• Implement consistent transformations for identifiers, tenancy, deduplication, and time-based logic
• Implement and maintain dbt tests for schema, freshness, and business logic validation
• Investigate and remediate issues surfaced by tests, monitoring, or product stakeholders
• Ensure models, columns, and metrics are well documented and aligned with standards
• Maintain tags, ownership metadata, and lineage annotations where applicable
• Build and maintain tables used by dashboards, segmentation, Ads Manager reporting, and embedded analytics
• Refactor ad hoc or feature-specific logic into standardized reusable assets
• Coordinate with Product Data Engineering and the CDP team on schema changes, backfills, and event data issues
• Partner with product engineers to validate outputs and support safe consumption patterns

Requirements:

• 4+ years of experience in analytics engineering or data engineering roles
• Strong hands-on experience with dbt and SQL, and comfort operating in production
• Proven experience building and maintaining reusable models with tests and documentation
• Familiarity with event-based/product data and multi-tenant modeling patterns
• Experience building production models for both warehouse analytics and low-latency analytics workloads

Success in This Role Looks Like:

• Customer-facing models remain correct, tested, and performant as product usage scales
• New product reporting needs are met primarily through reusable assets, not one-off logic
• Data issues are caught early and resolved systematically
• Product teams can move faster because they build on standardized datasets