6+ years in data science, analytics engineering, data engineering, or a related role. You've been close enough to real data work to know what should and should not be automated.
Deep SQL and analytics judgment - you can reason through metrics, experiments, data models, and messy warehouse reality without relying on a tool to think for you.
Strong product sense - you understand what stakeholders actually need, what makes a workflow adoptable, and how to turn a prototype into a product people use.
Production-oriented Python ability - you can build and ship working tools, wrangle APIs, evaluate model outputs, deploy services, and write code others can maintain.
Hands-on LLM experience - you've built with frontier models, agents, RAG systems, evals, or AI-powered workflows and have opinions about where they work and where they fail.
Pipeline and modeling fluency - you've worked with dbt, warehouse schemas, data quality issues, and the practical tradeoffs behind durable data systems.
Builder mentality - you see a manual process and immediately think about how to systematize it. You ship fast, measure quality, and iterate.
Autonomy - this is a new function. You'll help define the roadmap as much as execute it.