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 1 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.
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 1 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 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
The company is an Equal Opportunity Employer. As an employer subject to affirmative action regulations, we invite you to voluntarily provide the following demographic information. This information is used solely for compliance with government record-keeping, reporting, and other legal requirements. Providing this information is voluntary and refusal to do so will not affect your application status. This data will be kept separate from your application and will not be used in the hiring decision.
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