HighLevel - Staff Analytics Engineer – Customer Data Platform

• Define and govern the product event taxonomy across services and applications • Partner with engineering teams to establish clear instrumentation contracts and naming standards • Own the modeling patterns that translate event collection pipelines into durable warehouse datasets • Ensure event data is reliable, deduplicated, and usable for analytics and modeling • Transform raw events into reusable behavioral datasets such as sessions, feature usage, funnels, retention cohorts, and customer journeys • Design models that enable product teams to analyze feature adoption, engagement, and lifecycle behavior • Maintain modeling patterns that support both exploratory analysis and production use cases • Define and maintain canonical entities such as Agency, Location, Contact, Conversation, Campaign, Spend, Usage, and Outcomes • Establish durable fact and dimension models that connect behavioral events to business entities • Ensure relationships between entities remain consistent and scalable across teams and product surfaces • Build warehouse models that power product analytics platforms • Ensure metrics in analytics tools and warehouse metrics resolve to the same definitions • Provide standardized datasets for funnels, cohorts, retention analysis, and product experimentation • Build behavioral and feature‑ready datasets used by data science for lifecycle modeling, experimentation, and prediction • Ensure datasets are stable, versioned, and reproducible for downstream ML workflows • Establish modeling patterns, dbt conventions, macros, and documentation standards used across analytics engineering • Design tenant‑safe models that support multi‑tenant workloads and high‑concurrency analytics • Partner with platform teams to ensure models are performant for both internal analytics and in‑app experiences • Define tests, freshness expectations, and invariants for behavioral datasets • Implement automated validation for event completeness and schema consistency • Partner with platform and engineering teams to detect and resolve issues before they impact analytics or customers • Establish reusable modeling patterns and best practices • Review work from analytics engineers and raise the bar for correctness, clarity, and maintainability • Help shape the long‑term architecture of the behavioral data platform • Product events across the platform follow a clear and consistent taxonomy • Event collection pipelines feeding the warehouse and OLAP systems produce reliable, analysis‑ready behavioral data • Product analytics tools, internal analytics, and customer‑facing reporting all resolve to the same underlying definitions • Product teams can analyze usage, funnels, and retention without building custom analytics logic • Data science teams rely on stable behavioral datasets rather than raw event streams • Canonical customer and product models become the default foundation for analytics and product features across HighLevel.