Analytics Engineer / Data Engineer (AI)

Location: Hybrid in San Francisco or New York City, United States

Salary: $175,000–$330,000 USD annually, plus equity. USD salary ranges apply only to U.S.-based positions; international compensation is based on the local market.

Company

Perplexity builds AI products and data systems that make information reliable, understandable, and useful for people and AI.

What you will do

• Design and maintain high-quality data models, marts, pipelines, and warehouse workflows.
• Contribute to warehouse architecture, permissions, performance, cost management, environments, and data lifecycle operations.
• Build documentation, semantic context, metadata, lineage, and retrieval patterns that make warehouse data AI-readable.
• Define dbt standards, dimensional modeling practices, naming conventions, testing, review processes, and governance controls.
• Automate data quality, root-cause analysis, test generation, maintenance, and repetitive data-team workflows with AI-assisted tooling.
• Partner with data science, engineering, product, finance, security, legal, and GTM teams to build durable and controlled data systems.

Requirements

• 6+ years of experience as an analytics engineer, data engineer, data scientist, or in a closely related role.
• Deep SQL expertise, including query correctness, performance, joins, grain, and edge cases.
• Production experience with dbt or a similar transformation framework, dimensional modeling, data contracts, testing, and analytical schema evolution.
• Experience building, maintaining, debugging, and improving production data pipelines and managing warehouse access, permissions, performance, or costs.
• Strong understanding of data ownership, access controls, privacy, retention, lineage, auditability, security, reliability, and maintainability.
• Ability to turn ambiguous analytical requirements into trusted models, metrics, reusable data assets, and production-quality systems with minimal oversight.

Nice to have

• Snowflake administration, optimization, cost management, or warehouse performance tuning.
• Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy and security reviews.
• Experience with Databricks, semantic or metrics layers, metadata systems, data catalogs, or modern data infrastructure.
• Python experience for data tooling, automation, orchestration, or quality checks.
• Experience as an early analytics engineer or data engineer at a high-growth startup.

Culture & Benefits

• Small data team with broad scope and autonomy to define standards, choose tools, and establish company-wide defaults.
• Work focused on building data infrastructure for both human users and AI agents.
• Full-time U.S. employees receive equity, health, dental, vision, retirement, fitness, commuter, and dependent-care benefits.
• Full-time employees outside the U.S. receive benefits tailored to their region of residence.