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Perplexity is building an AI answer engine and search product for consumers and enterprises. The Data team is seeking a Staff-level Analytics Engineer to design and own the core data foundation that powers the entire organization.
You will be responsible for building reliable, understandable, and usable data systems that serve both human analysts and AI agents. This includes designing dimensional data models, building and maintaining production pipelines, managing warehouse architecture and performance, establishing data governance practices, and ensuring security and privacy compliance. You'll define dbt standards, dimensional modeling practices, and testing frameworks that become company-wide defaults. A key part of this role is making the warehouse AI-readable through documentation, semantic context, metadata, and retrieval patterns that AI systems can understand and query correctly.
You'll also lead efforts to automate data quality detection, improve data team productivity through tooling and workflow automation, and partner across engineering, product, finance, and GTM teams to translate analytical needs into durable systems. You'll evaluate build-versus-buy decisions for data tools and manage vendor relationships as the data stack scales.
This is a high-leverage role at a high-growth AI company where your work directly determines how fast and confidently the organization can use data. You'll operate at the intersection of analytics engineering, data engineering, data governance, and internal product, with significant autonomy to define standards and ship systems that become organizational defaults.
REQUIREMENTS:
- 6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role
- Deep SQL expertise with ability to reason about correctness, performance, joins, grain, and edge cases in complex queries
- Strong hands-on data modeling experience with dbt (or similar transformation framework) in production; understanding of dimensional modeling, data contracts, testing, and schema evolution
- Proven pipeline ownership: building, maintaining, debugging, and improving production data pipelines
- Warehouse management experience including administration, access patterns, permissions, performance tuning, cost management, or operational ownership
- Governance mindset with clear thinking about data ownership, access controls, privacy, retention, lineage, auditability, and access risk management
- AI-native working style: already using AI to speed up development, documentation, QA, exploration, and workflow automation
- Stakeholder fluency: ability to translate messy analytical requirements into trusted models, metrics, and reusable data assets
- Autonomy and execution: can take projects from ambiguous problem to production-quality system with minimal oversight
- Operational judgment: care about reliability, governance, security, cost, and long-term maintainability
BONUS QUALIFICATIONS:
- Snowflake administration, optimization, cost management, or performance tuning
- Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy/security reviews
- Databricks or other modern data infrastructure experience
- Building semantic layers, metrics layers, metadata systems, or data catalogs
- Python experience for data tooling, automation, orchestration, or quality checks
- Early-stage analytics engineer or data engineer experience at high-growth startups
About Perplexity AI
AI / Data / Infrastructure — AI answer engine and search product for consumers and enterprises.
