Principal Analytics Engineer

For over 30 years, Angi has powered the future of the home services industry, creating an environment where homeowners and pros benefit from more jobs done well. For homeowners, our platform is a reliable way to find skilled pros. For pros, we're a reliable business partner who helps them find the winnable work they want, when they want. For employees, we're an amazing place to call home. We can't wait to welcome you. Angi at a glance: Founded in 1995 as Angie’s List and rebranded in 2021 Global company with 9 brands in 8 countries and employees worldwide Homeowners have turned to us for 300 million home projects and counting About the team The Principal Analytics Engineer for Product Analytics will shape how product data is transformed and consumed, ensuring that both human analysts and AI agents receive consistent metrics, faster iteration, and trustworthy answers. This role is central to designing and evolving an analytics architecture that radically shortens the distance from complex business questions to validated insights. As a Principal leader, you will be the connective tissue across data engineering, analytics, and product teams—architecting pipelines, semantic layers, and quality practices so they work as a singular, cohesive system. The ideal candidate is a visionary technical simplifier with deep expertise in modern data stacks, a passion for developer/analyst velocity, and a proven ability to enable partner teams. You will play a dual role: empowering human analysts to spend less time debugging and more time driving strategy, while simultaneously hardening our data layer into a load-bearing, semantic infrastructure that AI tools can query accurately and safely. What you’ll do Architecture & Semantic Layer Strategy Data Product Ownership: Design, build, and evolve high-scale dbt models that transform raw upstream inputs into clean, well-documented, analytics-ready data products with clear contracts and ownership. Agent-Safe Infrastructure: Implement critical guardrails, clear grain definitions, meaningful metadata descriptions, and approved access paths so AI tools and agents can query data accurately without bypassing governance. Semantic Evolution: Shape how metrics are defined and exposed so that analysts, dashboards, and AI tools all return the same answer to the same question, turning the semantic layer into production-grade infrastructure for AI-powered BI. Operational Excellence & Velocity Friction Elimination: Translate recurring analyst pain points—such as ambiguous metrics, broken joins, or undocumented fields—into durable, reusable models, patterns, and shared interfaces. Quality & Governance Standards: Set and hold the bar for what "done" means for a data product, embedding robust testing, freshness expectations, data catalogs, and metadata ownership into the development lifecycle. Cross-Functional Alignment: Coordinate across data platforms and product teams to map use cases, eliminate duplicate work, surface