Analytics Engineer

Details

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.

We're looking for someone who's been a great data scientist, analytics engineer, or data engineer — someone who knows which metric actually matters, can design A/B tests that answer the real question, has gone deep on a data model when something didn't add up, and who has decided the highest-leverage next step is to build AI systems that fundamentally change how data science gets done.

Not another text-to-SQL bot or another dashboard. You'll build AI agents that conduct full analyses autonomously — forming hypotheses, writing and running queries, interpreting results, and drafting recommendations. You'll make the entire data warehouse AI-readable so any system can query it accurately. You'll create self-healing pipelines that detect and fix data issues before anyone notices. You'll build the infrastructure that turns a small data team into one that operates at 10x its size.

The data team is already using AI across workflows; this role is dedicated to turning that work into world-class, scalable systems, new tools, and an AI-native way of working.

Responsibilities

• Accelerate the AI-native data workflow: turn working approaches into repeatable systems, scalable tools, and patterns for the data team and company.
• Build AI agents that do end-to-end data science: 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 so AI systems can query Perplexity's data accurately and reliably.
• Automate the data lifecycle: implement self-healing pipelines, automated dbt model generation and validation, and data quality agents that detect, diagnose, and fix issues autonomously.
• Ship AI-powered experiment analysis: build agents that interpret A/B test results, flag statistical issues, and draft ship/no-ship recommendations for product teams.
• Own the full lifecycle: from problem identification to prototyping with LLMs, iterating on accuracy and UX, to production deployment and monitoring.
• Turn the data team into a product team: build internal data products and self-serve AI interfaces to replace ad-hoc requests.

Requirements

• 6-8+ years in data science, analytics engineering, or a related role — extensive hands-on experience in data.
• Strong product sense: experience working closely with product and business teams, and good instincts for what to measure and build.
• Deep SQL expertise: you think in SQL, have built data models, and know your way around a warehouse.
• Pipeline experience: built and maintained data pipelines, worked with dbt, and handled data quality issues firsthand.
• Enough software engineering chops to be dangerous: can build and ship a working tool in Python (not just a notebook), wrangle APIs, deploy a service, and write maintainable code.
• Genuinely excited about AI: built with LLMs, have opinions about models, tried building agents, RAG systems, or AI-powered workflows.
• Builder mentality: automate manual processes, ship fast, and iterate.
• Autonomy: define the roadmap and execute it in a new function.

Bonus

• Experience with dbt (building and maintaining production models).
• Snowflake administration and optimization.
• Built Slack bots, internal CLI tools, or developer productivity tools that were used in production.
• Background in AI agent frameworks.
• Experience with BI tools and manual BI workflows.
• A/B testing and experimentation experience (designing experiments and analyzing results).
• Early-stage startup experience.

Benefits

• Full-time U.S. employees: comprehensive benefits including equity, health, dental, vision, retirement, fitness, commuter and dependent care accounts, and more.
• International employees: comprehensive benefits tailored to region of residence.
• USD salary ranges apply to U.S.-based positions; international salaries are set based on the local market.