Member of Data Staff (Data Acceleration)Active$175K–$330K

Perplexity
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The opportunity

Perplexity is AI for people who expect more. This role brings that same standard to how our data team works, with AI at the center of everything we do.

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.

What they're looking for

  • + 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.