AI Analytics Engineer

About Havenly Brands

Havenly Brands is a family of home design and furnishings companies united by a single mission: to make beautiful, personalized home design accessible to everyone. Our portfolio includes Havenly, Interior Define, Burrow, The Citizenry, The Inside, and St. Frank—spanning AI-powered interior design services, custom furniture, and globally-sourced artisan home goods. With nearly a million designs created and a team of expert interior designers powered by cutting-edge technology, including AI visualization and 3D rendering tools, we’re transforming how people discover, design, and furnish their homes.

Headquartered in Denver with offices in New York, we’re building the future of home at the intersection of technology, design, and consumer experience.

We are hiring an AI Analytics Engineer to lead the build‑out of data infrastructure and intelligent systems across Havenly Brands. This is a highly strategic role that sits at the intersection of analytics engineering, data engineering, and AI development, focused on evolving our data practice for an AI‑first world. You will partner with business leaders to design, build, and implement end‑to‑end data solutions.

Your technical focus will span from foundational data pipelines and semantic layers—requiring expert proficiency in data processing, analysis, SQL, and dbt—to architecting systems optimized for AI consumption. Crucially, you will build data interfaces that allow AI agents to reliably query business information. This involves leveraging tools like MCP (Model Context Protocol servers), LLM context windows, and internal applications to enable fast, conversational access to a single source of truth, moving beyond traditional dashboards.

You will translate business strategy into measurable data outcomes and report to the SVP of Product and Technology.

• Collaborate directly with business leaders to understand strategy, operations, and the outcomes they're trying to achieve—not just the data they think they need
• Design analytical approaches and measurement frameworks that directly target business goals, going beyond data modeling to the end analysis and action
• Lead measurement implementation strategy: determine what data we should collect, can collect, and how to instrument new touchpoints to answer emerging questions
• Translate business problems into data solutions, and data insights back into business recommendations

• Extract data from disparate sources into a centralized cloud data warehouse
• Design and implement data pipelines that ensure reliability, correctness, and freshness
• Build and maintain transformations and semantic layers that accurately model business operations across our portfolio of brands
• Implement data quality monitoring, testing, and observability across all data assets

• Architect data systems optimized for AI agent consumption, not just traditional BI tools
• Build and configure semantic layers, MCPs (Model Context Protocol servers), and interfaces that allow AI systems to reliably query and understand our data
• Create governed, well‑documented metrics and dimensions that serve as a single source of truth for both humans and AI systems
• Develop tools and context that empower internal AI agents and interactive AI applications
• Drive strategy and implementation of consumer touchpoint data collection to power internal intelligence and cross‑brand marketing activation
• Identify opportunities to automate operations with AI and data processing—then build the solutions
• Build internal tools and applications that leverage AI capabilities to serve the organization
• Help uplevel the entire organization's data capabilities and AI fluency

• 3-5+ years of experience in analytics engineering, data engineering, or a related field working with business stakeholders
• Expert SQL skills with experience writing complex transformations, data models, and analytical queries
• Strong Python proficiency for data processing, automation, and building tools
• Modern data stack experience: dbt (strongly…