Analytics Engineer, Thesis

• Strong SQL. You've written real queries against real warehouses. Not notebook exercises
• Hands-on experience with data pipelines and ETL, including the unglamorous work of keeping them running
• Experience with a BI tool such as Metabase, Looker, Tableau, or equivalent
• Hands-on experience building with LLMs: prompting, retrieval, context management, tool use, and agentic patterns
• Solid fundamentals in databases and APIs
• 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
• Comfort with ambiguity and a genuine preference for ownership over tickets
• 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
• 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

Desirable

• We're looking for an Analytics Engineer to own our data infrastructure and build the AI systems that change how this company operates
• This is a hybrid role by design. Half of it is making sure the data this business runs on is reliable, accessible, and fast: the pipelines feeding our warehouse, the reporting layer every team depends on, and the endless stream of questions from Ops, Marketing, and Growth
• The other half is building the agents and automations that let a small team operate like a much larger one. That second half is not a side project or a someday. It's why this role exists in the shape it does, and it's where the person in this seat will grow
• You'll work directly with our Director of Data Analytics, who owns the company's data and AI mandate. This is a small team with an enormous surface area, so you'll touch everything from a broken ETL job to an agent architecture nobody has built before
• Own the data foundation
• Maintain and optimize the ETL pipelines feeding our Snowflake warehouse and Metabase reporting layer
• Keep reporting trustworthy. Diagnose anomalies, fix breakages, and improve the things that keep breaking
• Build and extend models, views, and dashboards that teams can actually self-serve on
• Be the internal data resource
• Absorb ad hoc requests from Ops, Marketing, and Growth and turn ambiguous business questions into SQL, models, and answers
• Work directly with stakeholders to understand what they're really asking, which is rarely what they first ask for
• Push the organization toward self-service instead of becoming a query queue
• Build AI into how the company works
• Design and ship AI agents and agentic workflows that reason, use tools, retrieve information, and execute multi-step tasks
• Build the skills, orchestration layers, and human-in-the-loop systems that let non-technical teams operate at 10x
• Integrate LLMs from Anthropic, OpenAI, and elsewhere into internal tooling and customer-facing products
• Build retrieval and semantic search over our ingredient library, research, and customer data
• Prototype fast, test with real users, and turn what works into something reliable
• Stay close to emerging AI research, models, and tooling, and determine what's actually useful versus hype