Analytics Engineer

Join our team as an Analytics Engineer, where you'll own our data infrastructure and build AI systems that transform our operations. This hybrid role involves maintaining and optimizing our data pipelines, ensuring reliable reporting, and building AI agents and automations. You'll work closely with our Director of Data Analytics and have a significant impact on the company's data and AI initiatives.

• Ownership of the data infrastructure, including maintaining and optimizing ETL pipelines, ensuring data reliability and accessibility.
• Building and extending models, views, and dashboards that teams can self-serve on, and turning ambiguous business questions into actionable insights.
• Designing and shipping AI agents and workflows that enhance operational efficiency, integrating LLMs into internal tooling and customer-facing products.

• Ability to move from an ambiguous business question to a working prototype to a reliable production system
• Strong product instincts and the ability to tell where AI creates real value versus where it's theater
• Strong SQL. You've written real queries against real warehouses. Not notebook exercises
• Hands-on experience building with LLMs: prompting, retrieval, context management, tool use, and agentic patterns
• Experience with a BI tool such as Metabase, Looker, Tableau, or equivalent
• Hands-on experience with data pipelines and ETL, including the unglamorous work of keeping them running
• Comfort with ambiguity and a genuine preference for ownership over tickets
• Solid fundamentals in databases and APIs
• Python and modern backend development experience
• Front-end or web development experience. Being able to support our website when needed is a bonus, not a requirement
• Experience with Snowflake specifically
• Experience with subscription, DTC, or e-commerce data models
• Experience with vector databases (pgvector, Pinecone, Qdrant, or similar) and RAG architectures
• Experience building evaluation or monitoring systems for LLM applications
• Experience with AWS, GCP, or Azure
• Contributions to AI research, open-source projects, or technical publications
• Experience at an early-stage or high-growth company
• You're comfortable operating with ambiguity, taking ownership of problems from idea through deployment, and moving quickly without sacrificing quality. You're excited by the chance to help define what an AI-native company looks like rather than adding AI features to existing workflows
• You'll thrive here if you're deeply curious about AI but fundamentally an engineer and a builder. You like experimenting with new technology, but you care even more about whether it works reliably in the real world
• One thing matters more to us than anything on the list above. We care less about what you already know than how you learn. The AI tooling landscape resets every few months, and the people who do well here are the ones running their own experiments, reading past the hype cycle, and showing up with approaches nobody asked them for. If you use AI the way most people use Google search, this will be a frustrating job