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

Perplexity
Apply now

The opportunity

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.

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.

What they're looking for

  • + years of experience as an analytics engineer, data engineer, data scientist, or closely related role.
  • Deep SQL expertise: you can reason about correctness, performance, joins, grain, and edge cases in complex warehouse queries.
  • Strong data modeling experience: you've worked hands-on with dbt (or a similar transformation framework) in production, and you understand dimensional modeling, data contracts, testing, and how analytical schemas should evolve.
  • Pipeline ownership: you've built, maintained, debugged, and improved production data pipelines.
  • Warehouse management experience: you've worked with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership.
  • Governance mindset: you think clearly about data ownership, access controls, privacy, retention, lineage, auditability, and the risks of making data too easy to access.
  • AI-native working style: you already use AI to speed up development, documentation, QA, exploration, and repetitive workflow automation.
  • Stakeholder fluency: you know how to turn messy analytical requirements into trusted models, metrics, and reusable data assets.