Revenue Systems Engineer

Role Purpose

Design, build, and own the production systems that power FrankieOne's revenue operations. The Revenue Systems Engineer is a senior technical role responsible for end-to-end ownership of data pipelines, AI/ML platforms, automation infrastructure, and internal tooling that enable the RevOps function to operate at scale. This is not a support or maintenance role — it is an engineering ownership role with direct business impact.

You will work within the Leverage Team, partnering closely with the Revenue Systems Engineer and the RevOps Manager to identify high-leverage problems and translate them into reliable, measurable production systems embedded in daily workflows across operations, management, sales, and client teams.

Key Responsibilities

AI & Machine Learning Systems (~35%)

• Design, build, and maintain end-to-end ML systems including training pipelines, model serving, and API deployment for revenue-impacting use cases (scoring, classification, prediction).

• Develop and operate AI-powered content generation and analysis systems that produce production-ready output at scale.

• Build evaluation pipelines, feedback loops, and regression monitoring frameworks to ensure ongoing model performance.

• Own the full lifecycle of deployed AI systems, including architecture, deployment, performance monitoring, and continuous improvement.

• Identify high-value automation opportunities across the revenue workflow and design AI-first solutions to address them.
  

Data Platform & Backend Engineering (~30%)

• Design and operate high-throughput data pipelines processing millions of records per day across both batch and real-time modes.

• Build and maintain backend APIs and processing services that integrate HubSpot, Xero, Redshift, and other revenue-critical systems.

• Architect scalable, low-latency data infrastructure that supports operational, analytical, and reporting needs.

• Develop commission calculation engines, financial reconciliation systems, and contract data extraction pipelines.

• Own data quality monitoring, alerting, and incident response for production systems.

Internal Platforms & Tooling (~20%)

• Build internal tools and dashboards (React, Python) that are adopted and used daily across sales, operations, and management teams.

• Develop email processing, workflow classification, and automation APIs that remove manual work from operational processes.

• Create reporting and analytics services for enterprise clients and account managers.

• Build training data systems and evaluation infrastructure that enable the team to develop and iterate on AI capabilities.

• Maintain and improve the existing portfolio of production RevOps tools with a focus on reliability and performance.

Engineering Standards & Collaboration (~15%)

• Take end-to-end technical ownership of projects, from architecture and scoping through to production deployment and ongoing operation.

• Document systems, APIs, and data models to reduce key-person dependency and enable team scaling.

• Establish and maintain engineering best practices including code review, testing, monitoring, and deployment standards.

• Partner with the Senior RevOps Manager to identify the highest-leverage technical investments and translate business needs into engineering specifications.

• Upskill and mentor the RevOps Manager in Python, automation, and AI tooling.