Staff Analytics Engineer – Customer Data Platform

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.Our PeopleWith 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 ImpactAs 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 a Staff Analytics Engineer to lead the modeling and semantic foundation of our Customer Data Platform. This role sits at the intersection of product data, analytics engineering, and data platform architecture. You will define how product events become structured behavioral datasets that power analytics, product insights, machine learning, and in‑app reporting. You will partner closely with product, engineering, marketing, data science, and platform teams to ensure that behavioral data is reliable, well‑modeled, and consistently defined across the company.Responsibilities:Define and govern the product event taxonomy across services and applicationsPartner with engineering teams to establish clear instrumentation contracts and naming standardsOwn the modeling patterns that translate event collection pipelines into durable warehouse datasetsEnsure event data is reliable, deduplicated, and usable for analytics and modelingTransform raw events into reusable behavioral datasets such as sessions, feature usage, funnels, retention cohorts, and customer journeysDesign models that enable product teams to analyze feature adoption, engagement, and lifecycle behaviorMaintain modeling patterns that support both exploratory analysis and production use casesDefine and maintain canonical entities such as Agency, Location, Contact, Conversation, Campaign, Spend, Usage, and OutcomesEstablish durable fact and dimension models that connect behavioral events to business entitiesEnsure relationships between entities remain consistent and scalable across teams and product surfacesBuild warehouse models that power product analytics platformsEnsure metrics in analytics tools and warehouse metrics resolve to the same definitionsProvide standardized datasets for funnels, cohorts, retention analysis, and product experimentationBuild behavioral and feature‑ready datasets used by data science for lifecycle modeling, experimentation, and predictionEnsure datasets are stable, versioned, and reproducible for downstream ML workflowsEstablish modeling patterns, dbt conventions, macros, and documentation standards used across analytics engineeringDesign tenant‑safe models that support multi‑tenant workloads and high‑concurrency analyticsPartner with platform teams to ensure models are performant for both internal analytics and in‑app experiencesDefine tests, freshness expectations, and invariants for behavioral datasetsImplement automated validation for event completeness and schema consistencyPartner with platform and engineering teams to detect and resolve issues before they impact analytics or customersEstablish reusable modeling patterns and best practicesReview work from analytics engineers and raise the bar for correctness, clarity, and maintainabilityHelp shape the long‑term architecture of the behavioral data platformRequirements:9+ years in analytics engineering, data engineering, or data architectureDeep expertise in SQL and dbt, including testing, documentation, and version‑controlled workflowsStrong experience modeling event‑based or product usage data at scaleExperience working with modern event collection systems and product analytics platformsProven ownership of canonical datasets or semantic layers used by multiple teamsStrong judgment around metric definitions, change management, and keeping data consistent across a growing platformSuccess in this role looks like:Product events across the platform follow a clea