Member of Data Staff (Analytics Engineer)
Perplexity
- Pay
- $175k–$330k
- Workplace
- On-site
- Location
- San Francisco · California · United States
- First seen
- 10 days ago
- Last seen
- 1 hour ago
- Board
- Ashby
Summary
Perplexity is AI for people who expect more.
Posting
Perplexity is AI for people who expect more. On the data team, that means building the systems that make our data reliable, understandable, and usable by both humans and AI. We're looking for an analytics engineer or data engineer who wants to build the foundation for an AI-native data organization. You'll design core data models, pipelines, semantic layers, data quality systems, governance practices, and warehouse workflows that power the entire company: helping teams make strategic decisions, operate the business, and move faster with trusted data. You'll also make sure those systems are secure, privacy-aware, and legible to AI agents, data scientists, and the rest of the company. This role is for someone who can operate at the boundary of analytics engineering, data engineering, data governance, and internal product. You care about dimensional modeling, dbt standards, cost-aware warehouse design, access controls, privacy, and the details that make data trustworthy. You also believe AI should make the data stack faster, easier to maintain, and more accessible across the company without weakening security or governance. WHAT YOU'LL DO - Build the core data foundation - design and maintain high-quality data models, marts, and pipelines that make analysis fast, reliable, and reusable. - Manage the data warehouse - help own warehouse architecture, environments, permissions, performance, cost, data lifecycle, and operational hygiene so the platform scales cleanly. - Make the warehouse AI-readable - own the documentation, semantic context, metadata, lineage, and retrieval patterns that AI systems depend on to understand and query Perplexity's data correctly. - Own data modeling standards - define and champion dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes. - Lead data governance practices - define standards for access, ownership, lineage, documentation, retention, quality, and sensitive data handling across the analytical warehouse. - Build with security and privacy in mind - partner with engineering, security, legal, and finance where needed to ensure data access, sharing, and AI-enabled workflows are appropriate and controlled. - Automate data quality and maintenance - build AI-assisted workflows that detect issues, explain root causes, suggest fixes, generate tests, and reduce manual firefighting. - Improve data team productivity - automate repetitive workflows, improve tooling, streamline d