• Lead the migration of fraud reporting and workflows into a fully automated, scalable data pipeline. • Design, build, and maintain analytics-ready data models using dbt that support fraud monitoring, investigation, and strategy. • Partner with Data Engineering to ensure reliable ingestion, data quality, and documentation of fraud-related datasets. • Develop and maintain dashboards and reporting that track key fraud metrics such as fraud rates and loss, chargebacks and disputes, bonus abuse, promo abuse, and multi-accounting, and KYC and account integrity signals. • Surface emerging fraud patterns and anomalies through proactive analysis. • Write performant, production-quality SQL across large, complex datasets. • Build modular, reusable dbt models that enable self-service analytics for Fraud & Risk teams. • Implement testing, freshness checks, and documentation to ensure trust in fraud data. • Work closely with Fraud & Risk, Payments, Customer Support, Product, and Engineering to align on definitions, metrics, and investigative workflows. • Provide actionable insights to fraud leadership to inform risk rules and thresholds, tooling and vendor evaluations, process improvements and operational tradeoffs.

• Collects, cleans, and analyzes data from various sources to identify trends, patterns, and insights • Develops and maintains SQL queries to extract and manipulate data for reporting and analysis • Creates and manages PowerBI dashboards and reports to visualize data and communicate findings to stakeholders • Collaborates with cross-functional teams to understand business requirements and provide data-driven solutions • Performs data validation and ensure data accuracy and integrity • Assists in the development and implementation of data analytics best practices and standards • Supports ad-hoc data analysis requests and provide actionable recommendations • Performs other projects, duties, and tasks, as assigned.

• Own the financial data and analytics infrastructure that powers HighLevel’s forecasting, reporting, and IPO-readiness efforts; ensure Finance has access to timely, accurate, and trusted data • Design, build, and maintain data products including models, dashboards, and reporting frameworks that deliver actionable insights to Finance and executive leadership • Lead KPI stewardship - develop and refine key performance metrics and financial data structures, ensuring consistency and accuracy across ARR, GRR, NRR, CAC, LTV, and other core SaaS KPIs • Write and optimize complex SQL queries to extract and transform large data sets from systems (billing, CRM, HRIS, data warehouse) into analysis-ready outputs • Partner cross-functionally with FP&A, Accounting, BI, and Data Engineering to align on data sources, definitions, and reporting standards • Drive the development of scalable, automated analytics processes, improving accuracy, efficiency, and speed-to-insight across Finance • Establish and uphold data governance and quality standards within the Finance domain, ensuring a single source of truth for critical metrics used across the business • Champion a data-driven culture within Finance, helping elevate analytical rigor and consistency as the company scales toward public-company readiness

• Define and execute a modern analytics strategy and multi year roadmap aligned to company objectives • Elevate data maturity across the organization, enabling real time and forward looking insights • Ensure analytics investments directly support strategic decision making, product innovation, and sustainable growth • Personally build and maintain strong, trusted relationships with senior leaders across Finance, HR, Marketing, Product, and Operations • Use these partnerships to inform analytics priorities, sequencing, and resource allocation • Act as a strategic advisor, proactively surfacing insights, trends, opportunities, and risks to influence executive decisions • Establish and govern clear, consistent KPIs tied to business outcomes • Design and oversee executive ready dashboards and reporting that drive accountability and action • Ensure insights are communicated with strong business context and clear recommendations • Own the definition, validation, and governance of core business metrics • Partner with Data Platforms and Engineering to improve data quality, accessibility, and self service capabilities • Serve as the primary bridge between business stakeholders and technical teams • Translate business questions into analytical frameworks and technical requirements • Communicate complex analytical concepts in a clear, concise, and actionable manner for diverse audiences • Develop and lead a high performing analytics organization grounded in excellence, accountability, and collaboration • Lead as an empathetic, hands on player coach who actively mentors team members • Invest in developing both deep analytics expertise and executive level communication, strategic thinking, and business acumen • Build sustainable performance systems through continuous feedback, growth plans, coaching, and intentional team building • Continuously assess analytics processes, tools, and operating models to improve efficiency and scalability • Implement best practices that enable the team to deliver high impact insights consistently as the company grows