AI Analytics Engineer

Havenly•Denver, CO

17h

About The Position

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.

Requirements

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 preferred), cloud warehouses (Snowflake, BigQuery, or similar), and ETL/orchestration tools (Airbyte, Fivetran, Airflow, or similar)
AI-native workflow: You actively use AI tools (Claude Code, Cursor, or similar) to accelerate your work—and you're excited to push further
Business acumen: Track record of partnering with stakeholders to understand requirements, design solutions, and deliver measurable outcomes
Business-obsessed: You think beyond data models to the analysis, operation, and end goal—you care about outcomes, not just deliverables
Strategic thinking: You see the big picture—connecting data capabilities to business strategy, short-term wins to long-term vision
Proactive and curious: You identify opportunities before being asked and stay ahead of emerging technologies
Clear communicator: You can translate between technical and business contexts, articulating the "why"; in terms that resonate across disciplines
Quality-obsessed: You care deeply about data correctness, documentation, and reliability
Ownership mentality: You take initiative, navigate ambiguity, and see problems through to outcomes

Nice To Haves

Experience building internal tools or applications (web apps, automations, AI-powered features)
Direct experience architecting data for AI consumption (e.g. semantic layers, MCPs, RAG-optimized systems) or with with AI agent frameworks
Background in machine learning model development or deployment
Experience with consumer/marketing data, customer data platforms, or marketing activation
Experience with Vibe Coding/Agentic Engineering—you've enjoyed building with AI, and you can't stop thinking about it

Responsibilities

Consult and Partner with the Business
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
Build and Maintain Data Infrastructure
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
Enable AI-First Data Access
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 Intelligence & Automation
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