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Perplexity is seeking a Staff-level data professional to lead the design and implementation of AI-native data systems that fundamentally transform how the company's data team operates. This is a new function within the organization, and you'll have significant autonomy to define the roadmap while executing high-impact projects.
You will build AI agents and internal systems that handle end-to-end analysis workflows: forming hypotheses, writing and running queries, interpreting results, and drafting recommendations with appropriate evaluation, review, and guardrails. Key responsibilities include:
- Develop AI agents capable of autonomous data science work—not just SQL copilots, but systems that safely explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations with clear human review loops.
- Build retrieval infrastructure and evaluation loops that enable AI systems to query the warehouse reliably, leveraging semantic context and metadata accurately.
- Transform existing AI-assisted workflows into scalable, repeatable systems and reusable tools that the entire data team can adopt.
- Automate the data lifecycle through self-healing pipelines, automated dbt model generation and validation, data quality agents, and diagnosis workflows that reduce manual firefighting.
- Create AI-powered experiment analysis systems that interpret A/B test results, flag statistical issues, identify likely drivers, and draft ship/no-ship recommendations.
- Build internal data products that replace ad hoc requests with self-serve AI interfaces, turning the data team into a product team.
- Own the full lifecycle from identifying high-leverage problems, prototyping with LLMs, evaluating accuracy, designing UX, shipping to production, and monitoring quality.
Perplexity's data team is already using AI across its work. Your mission is to turn those individual workflows into scalable systems and an AI-native operating model that becomes a benchmark for how modern data teams operate.
REQUIREMENTS:
- 6+ years in data science, analytics engineering, data engineering, or related role
- Deep SQL and analytics judgment; ability to reason through metrics, experiments, data models, and warehouse complexity without relying on tools to think for you
- Strong product sense; understanding of what stakeholders need, workflow adoptability, and how to turn prototypes into products people use
- 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 fail
- Pipeline and modeling fluency; experience with dbt, warehouse schemas, data quality issues, and practical tradeoffs behind durable data systems
- Builder mentality; you see manual processes and immediately think about systematization, ship fast, measure quality, and iterate
- Autonomy and comfort defining roadmap in a new function
BONUS QUALIFICATIONS:
- 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
- Strong experimentation background including metric design and statistical interpretation
- BI tools experience with judgment about automation versus human review
- Early-stage startup experience
