AI Analytics Engineer, Havenly Brands

• 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

Desirable

• 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 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