Member of Data Staff (Analytics Engineer)

About the Role

Perplexity is looking for an analytics engineer or data engineer 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—ensuring systems are secure, privacy-aware, and legible to AI agents, data scientists, and cross-functional teams.

Responsibilities

• 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.
• Make the warehouse AI-readable: Own the documentation, semantic context, metadata, lineage, and retrieval patterns that AI systems depend on.
• 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.
• Build with security and privacy: Partner with engineering, security, legal, and finance to ensure appropriate data access and controlled AI workflows.
• Automate data quality: Build AI-assisted workflows that detect issues, explain root causes, suggest fixes, generate tests, and reduce firefighting.
• Improve data team productivity: Automate repetitive workflows, improve tooling, and streamline development for data scientists and stakeholders.
• Partner across the company: Collaborate closely with data scientists, engineering, product, finance, and GTM teams.
• Shape tooling decisions: Evaluate build-versus-buy tradeoffs, manage vendor relationships, and choose tools that scale.

Requirements

• 6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role.
• Deep SQL expertise, including reasoning about correctness, performance, joins, grain, and edge cases in complex warehouse queries.
• Strong data modeling experience with dbt (or similar transformation frameworks) in production, dimensional modeling, and data contracts.
• Proven track record of owning, maintaining, and debugging production data pipelines.
• Experience with data warehouse administration, access patterns, permissions, performance tuning, and cost management.
• Strong data governance mindset regarding data ownership, access controls, privacy, retention, and lineage.
• AI-native working style using AI tools to accelerate development, documentation, and automation.
• Excellent ability to translate ambiguous analytical requirements into trusted models and assets with high autonomy.

Bonus Qualifications

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