Lead Analytics Engineer

About HighLevel: HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes. To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently. Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability. Our People With over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home. Our Impact Every month, HighLevel enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact. We’re proud to be a part of that. Learn more about us on our YouTube Channel or Blog Posts About the Role: As a Lead Analytics Engineer focused on Revenue & Financial Modeling, you will own the end-to-end modeling of subscription revenue and financial KPIs. You will work closely with Finance to translate business definitions into precise, testable logic, and you will be accountable for ensuring those outputs reconcile to source systems such as Stripe. You will operate within the architectural and governance standards defined by the Analytics Engineering team, while owning the Revenue domain as an execution and implementation leader. Your work will directly power ARR, MRR, NRR, churn, customer counts, and other metrics used in executive reporting and external disclosures. Responsibilities:

Design and maintain the canonical revenue and subscription data model, centered on Stripe

Model subscription lifecycles including upgrades, downgrades, renewals, cancellations, refunds, and disputes

Implement ARR, MRR, NRR, churn, and customer/account counts as tested, versioned dbt models

Partner with Finance to translate business definitions into precise, production-grade SQL logic

Build and maintain reconciliation logic between dbt models, Stripe, and Finance-owned reports

Investigate and resolve discrepancies surfaced during reconciliation and downstream use

Own the technical correctness of revenue numbers used in executive and external reporting

Own data quality for all revenue and financial models, including test coverage and issue investigation

Ensure revenue models adhere to Analytics Engineering standards for documentation, lineage, ownership, and catalog synchronization

Participate in governed change workflows for critical revenue assets, ensuring changes are reviewed, traceable, and auditable

Apply sound engineering judgment when balancing correctness, reliability, and delivery speed

Establish a durable revenue and KPI foundation in the near term

As the foundation stabilizes, improve performance, maintainability, and usability of revenue models

Over time, support forecasting, cohort analysis, and advanced revenue analytics, and contribute revenue-domain expertise to broader Analytics Engineering initiatives

Work closely with Finance as the technical owner of revenue modeling

Coordinate with Data Engineering on ingestion, backfills, and schema changes across Stripe and other revenue-related source systems

Support BI and Analytics teams to ensure revenue models are usable and performant

Requirements:

7+ years of experience in analytics engineering, data engineering, or similar roles

Hands-on experience modeling subscription or usage-based revenue, ideally using Stripe

Proven ownership of financial or investor-facing metrics implemented in code (not spreadsheets)

Advanced SQL and dbt experience in a modern data warehouse such as Snowflake

Experience reconciling modeled outputs to source systems and financial reports

Comfortable partnering directly with Finance and owning the technical implementation of their definitions

Strong discipline around testing, documentation, and maintainability

Success in this role looks like:

Stripe revenue data is modeled once and reused everywhere

ARR, MRR, NRR, and churn are produced by tested dbt models that reconcile to source systems

Revenue models are reliable, governed, and safe to evolve over time

Finance and Analytics depend on the data platform as the single source of truth for revenue metrics