Analytics Engineer, Business Solutions
Vertex Service Partners
| Company | Vertex Service Partners |
| Category | Uncategorised |
| Location | Charlotte |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 21 Jul 2026 |
| Last verified | 2 Aug 2026 |
| Source | Employer career page (greenhouse) |
Description
About Us
Vertex Service Partners is a home improvement services company focused on residential roofing and other exterior services across the United States. Backed by Alpine Investors, a top-decile private equity fund with $4.0 billion of committed capital, Vertex is building a best-in-class national platform. Our company is built on our core values— servant leadership, unwavering character, a growth mindset, persistence, empowerment, pace, and fun —and guided by three pillars: being the Employer of Choice , Partner of Choice , and Contractor of Choice . We offer transformative support in operations, marketing, training, talent, finance, and technology, all while preserving the autonomy of local brands. The Analytics Engineer, Business Solutions serves as the bridge between how Vertex's brands operate and the data models, dashboards, and internal applications built to support them. This role partners closely with FP&A, Marketing, Operations, and the Data & AI Engineering team to transform ambiguous business problems into trusted analytics solutions, lightweight applications, and automation opportunities. The Analytics Engineer owns simpler business solutions end-to-end while providing scalable foundations for more complex engineering initiatives.
Key Responsibilities
Business Partnership & Intake: Serve as the primary point of contact for data and business intelligence requests from FP&A, Marketing, Operations, and other stakeholders. Gather business requirements, triage requests, and prioritize work through a shared intake process.
Dashboard Development & Insights: Build and maintain dashboards and analytical reporting that provide actionable business insights and improve decision-making across the organization.
Data Modeling: Develop gold-layer data models using dbt and advanced SQL, transforming business requirements into clean, scalable datasets that support dashboards, reporting, and internal applications.
Internal Application Development: Build lightweight internal applications using AI-assisted development tools and modern development practices. Utilize GitHub and CI/CD workflows to deliver maintainable business solutions.
Business Process Automation: Partner with business stakeholders to identify manual workflows and implement lightweight automation solutions that improve operational efficiency.
Business Validation: Validate data and reporting against operational source systems, including ServiceTitan, ensuring business users trust the accuracy and integrity of reporting outputs.
Requirements Translation: Translate ambiguous business requests into clearly defined technical requirements and actionable implementation plans.
Continuous Improvement: Identify opportunities for AI, automation, and reusable business logic that reduce manual work and improve operational effectiveness across the organization.
Key Outcomes
Deliver trusted dashboards, data models, and internal applications that improve business decision-making.
Independently own the delivery of straightforward analytics and application requests while providing production-ready foundations for more complex engineering initiatives.
Improve stakeholder confidence in business reporting through validation against operational source systems.
Reduce manual work across the organization by identifying and enabling automation opportunities.
Build strong partnerships with FP&A, Marketing, Operations, Sales, and Leadership by translating business needs into effective technical solutions.
Qualifications & Characteristics
Required:
3+ years of experience as a Data Analyst, BI Analyst, Business Analyst, or Analytics Engineer.
Strong SQL skills with experience building data models from business requirements.
Experience developing data models using dbt or the ability to quickly learn modern data transformation frameworks.
Experience