Member of Data Staff

What You'll Do

• Build AI agents that do data science - not just SQL copilots, but systems that can safely explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations with clear evaluation and human review loops.
• Make AI systems query the warehouse reliably - build the retrieval infrastructure and evaluation loops that let agents use our semantic context and metadata accurately.
• Accelerate the AI-native data workflow - turn the best existing AI-assisted workflows into repeatable systems, reusable tools, and patterns the whole data team can adopt.
• Automate the data lifecycle - build self-healing pipelines, automated dbt model generation and validation, data quality agents, and diagnosis workflows that reduce manual firefighting.
• Ship AI-powered experiment analysis - build agents that interpret A/B test results, flag statistical issues, identify likely drivers, and draft ship/no-ship recommendations.
• Turn the data team into a product team - build internal data products that stakeholders use every day, replacing ad hoc requests with self-serve AI interfaces.
• Own the full lifecycle - identify high-leverage problems, prototype with LLMs, evaluate accuracy, design the UX, ship to production, and monitor quality over time.

What We're Looking For

• 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.

Bonus

• Experience building production AI agents or agent evaluation systems.
• Experience with Snowflake, semantic layers, or metadata systems.
• Experience building internal tools, Slack bots, CLIs, or developer productivity products that people actually used.
• Strong experimentation background, including metric design and statistical interpretation.
• Experience with BI tools and the judgment to know what should be automated versus kept human-reviewed.
• Early-stage startup experience.