Lead the modeling of the Customer Data Platform, defining event taxonomy and ensuring reliable datasets for analytics, product insights, and machine learning. Collaborate with various teams to maintain data consistency and shape the architecture of the behavioral 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 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 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.
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 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.
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 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.
#LI-Remote #LI-NJ1
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.
#LI-Remote #LI-NJ1
Top Skills
Dbt
SQL
Similar Jobs
Fintech • Professional Services • Consulting • Energy • Financial Services • Cybersecurity • Generative AI
Manage projects in the transaction banking sector with an emphasis on compliance and retail payments, using Agile methodologies and strong stakeholder management.
Top Skills:
ConfluenceIso20022JIRA
.jpeg)
Artificial Intelligence • Hardware • Information Technology • Machine Learning
Responsible for inventory management, conducting audits, ensuring accuracy of shipments, and maintaining a safe working environment. Requires proficiency in logistics and inventory control, alongside strong communication skills.
Top Skills:
Ai TechnologiesExcelStatistical Process Control
Artificial Intelligence • Hardware • Information Technology • Machine Learning
The Material Specialist is responsible for material handling, documenting projects, inventory reconciliation, and communicating with various departments to resolve issues.
Top Skills:
SAP
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
