Staff Analytics Engineer at HighLevel

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. Responsibilities: • Own the enterprise data model: • Define and maintain canonical entities (Account, Customer, Location, Usage, Invoice, etc.) and their relationships across systems • Drive alignment between product, analytics, finance, and marketing data domains • Architect and maintain the dbt semantic layer: • Build modular, tested, and versioned dbt models with rigorous standards for naming, documentation, and lineage • Manage exposures to ensure all metrics and dashboards trace back to tested sources • Govern KPI and metric definitions: • Partner with Finance and BI to define and codify key company metrics (ARR, NRR, CAC payback, etc.) and enforce change control through versioned definitions • Enforce data contracts and schema governance: • Define and validate schemas, event structures, and data types for all inbound systems • Implement CI/CD tests to block breaking changes and maintain cross-system consistency • Drive observability and data quality standards: • Integrate dbt tests and freshness SLOs with the data catalog • Implement automated monitoring and alerting for data breaks and policy violations • Build the bridge between data and compliance: • Collaborate with Legal, IT, and Internal Audit to ensure IPE (information produced by the entity) lineage, evidence retention, and SOX readiness • Mentor and multiply: • Set technical direction and review standards in close partnership with the broader data organization • Define reusable macros, patterns, and documentation conventions that raise the bar for quality and reliability • Partner cross-functionally: • Work closely with data engineering on ingestion and contracts, BI on dashboard alignment, and Finance on KPI integrity • Influence how new systems and features are instrumented at the source to keep the data layer consistent Requirements: • 9+ years in data engineering, analytics engineering, or related roles with deep experience modeling data in dbt, Snowflake, or similar modern stacks • Proven ownership of an enterprise-scale data model or semantic layer used across multiple business functions • Advanced SQL and dbt skills; experience with CI/CD, testing frameworks, and Git-based workflows • Experience defining and enforcing data contracts, quality tests, and governance standards • Familiarity with SOX controls, audit evidence, or IPE lineage (experience in a public or IPO-bound company a plus) • Strong communication skills with the ability to translate between engineering, finance, and compliance stakeholders • Comfortable working in environments where precision, auditability, and trust in data are mission-critical Success in this role looks like: • A single, versioned enterprise data model used across Finance, Product, and GTM • Zero metric drift between dbt models and executive dashboards • Auditable lineage and IPE coverage across every dataset used in external reporting • Smooth adoption of modeling and governance standards by other data teams EEO Statement: 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 recordkeeping, 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. #LI-Remote #LI-NJ1