Member of Data Staff Analytics Engineer
PerplexityVisit Perplexity website
Perplexity is an AI-powered answer engine that provides real-time, cited answers and research capabilities.
San Francisco, United States
Funding history
About Perplexity
Private AI company founded in 2022. Its products combine conversational search and research with cited web sources; it also offers developer-facing Search, Agent, Router, and Embeddings APIs.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design and maintain data models, marts, pipelines, semantic layers, and warehouse workflows. You will establish dbt standards, governance, access controls, data quality automation, and AI-readable metadata. You will improve tooling, evaluate vendors, and translate cross-functional analytical needs into durable data systems.
Requirements
- 6+ years of experience in analytics engineering, data engineering, data science, or a related role
- SQL expertise
- Production experience with dbt or a similar transformation framework
- Dimensional modeling, data contracts, testing, and analytical schema design experience
- Production data pipeline ownership experience
- Warehouse administration, access, permissions, performance, cost, or operational ownership experience
- Knowledge of data governance, access controls, privacy, retention, lineage, and auditability
- Experience using AI for development, documentation, QA, exploration, and workflow automation
- Ability to translate analytical requirements into reusable data assets
- Ability to deliver production-quality systems with minimal oversight
Responsibilities
- Design and maintain high-quality data models, marts, and pipelines
- Manage warehouse architecture, permissions, performance, cost, and data lifecycle
- Create AI-readable documentation, semantic context, metadata, lineage, and retrieval patterns
- Define dbt patterns, dimensional modeling practices, tests, and review processes
- Establish data governance standards for access, ownership, retention, quality, and sensitive data
- Build security- and privacy-aware data workflows
- Automate data-quality detection, diagnosis, testing, and maintenance
- Improve data tooling and automate repetitive workflows
- Translate analytical needs into durable data systems
- Evaluate build-versus-buy tradeoffs and manage vendors
Benefits
- Equity
- Health insurance
- Dental insurance
- Vision insurance
- Retirement benefits
- Fitness benefits
- Commuter accounts
- Dependent care accounts
- Regionally tailored benefits for eligible international full-time employees