6-8+ years in data science, analytics engineering, or a related role
Strong product sense and understanding of user behavior
Deep SQL expertise with experience in data modeling
Experience in building and maintaining data pipelines, particularly with dbt
Proficient in software engineering for developing functioning tools in Python
Passionate about AI with hands-on experience in building AI agents
Builder mentality with a knack for automating manual processes
Ability to define and execute a new roadmap with autonomy
Responsibilities
Accelerate the AI-native data workflow by creating repeatable systems and scalable tools
Build AI agents that conduct end-to-end data analyses independently
Make the data warehouse AI-readable for accurate querying by any AI system
Automate the data lifecycle with self-healing pipelines and quality agents
Ship AI-powered analyses of experiments, providing actionable insights
Own the full lifecycle of AI projects from ideation to monitoring
Transform the data team into a product team with self-serve AI interfaces
Benefits
Opportunity to set industry standards in AI data workflows
Alignment between products and internal AI systems for high impact
Access to frontier AI models and resources from day one
Ability to significantly leverage efforts of data teams
Fast-paced environment with rapid implementation of ideas
Full Job Description
What You'll Do
Accelerate the AI-native data workflow - the team is already working this way. You'll take what's working and turn it into repeatable systems, scalable tools, and patterns that the data team and the entire company can adopt
Build AI agents that do data science - not just answer SQL questions, but conduct end-to-end analyses: explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations
Make the warehouse AI-readable - build the semantic layer, context, and retrieval infrastructure that lets any AI system (internal or product) query Perplexity's data accurately and reliably
Automate the data lifecycle - self-healing pipelines, automated dbt model generation and validation, data quality agents that detect, diagnose, and fix issues autonomously
Ship AI-powered experiment analysis - agents that interpret A/B test results, flag statistical issues, and draft ship/no-ship recommendations for product teams
Own the full lifecycle - from identifying the highest-leverage problem, to prototyping with LLMs, to iterating on accuracy and UX, to production deployment and monitoring
Turn the data team into a product team - build internal data products that stakeholders across the company actually use daily, replacing ad-hoc requests with self-serve AI interfaces
What We're Looking For
6-8+ years in data science, analytics engineering, or a related role - you've been in the data trenches
Strong product sense - you've worked closely with product and business teams, you understand what drives user behavior, and you have good instincts for what to measure and what to build
Deep SQL expertise - you think in SQL, you've built data models, you know your way around a warehouse
Pipeline experience - you've built and maintained data pipelines, worked with dbt, dealt with data quality issues firsthand
Enough software engineering chops to be dangerous - you can build and ship a working tool in Python, not just a notebook. You can wrangle APIs, deploy a service, write code that other people can maintain. You're not a SWE, but you're not afraid of production
Genuinely excited about AI - you've been building with LLMs on your own time. You have opinions about which models are good at what. You've tried building agents, RAG systems, or AI-powered workflows. You follow the space obsessively because you think it's going to change everything - including how data teams work
Builder mentality - you see a manual process and you can't help but automate it. You ship fast and iterate
Autonomy - this is a new function. You'll define the roadmap as much as execute it
Bonus
Experience with dbt (building and maintaining production models)
Snowflake administration and optimization
You've built Slack bots, internal CLI tools, or developer productivity tools that people actually used
Background in AI agent frameworks
Experience with BI tools - you know what's worth automating because you've done the manual version
A/B testing and experimentation - you've designed experiments and analyzed results
Early-stage startup experience
Why This Role
Set the standard for the industry - the team is already using AI across its work. You'll be the one who turns that into something other data orgs look to as the benchmark
Recursive AI - Perplexity builds an AI answer engine for the world. You'll build one for the company. Few places offer this kind of alignment between the product and the work
Frontier models, day one - you're at an AI company with access to frontier infrastructure and people who deeply understand what's possible
Massive leverage - the systems you build will multiply the output of every data team member and every stakeholder who needs data
Direct impact - small team, no layers of approval. Idea to shipped system in days, not quarters
