India onlyAbout 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 People
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 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
Requirements:
- 9+ years in analytics engineering, data engineering, or data architecture
- Deep expertise in SQL and dbt, including testing, documentation, and version‑controlled workflows
- Strong experience modeling event‑based or product usage data at scale
- Experience working with modern event collection systems and product analytics platforms
- Proven ownership of canonical datasets or semantic layers used by multiple teams
- Strong judgment around metric definitions, change management, and keeping data consistent across a growing platform
Success in this role looks like:
- 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.
United States