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Perplexity is seeking a Staff Analytics Engineer to build the data foundation for an AI-native organization. You'll design and own core data models, pipelines, semantic layers, data quality systems, governance practices, and warehouse workflows that power strategic decision-making across the company. This role operates at the intersection of analytics engineering, data engineering, data governance, and internal product—requiring someone who can move fluidly between dimensional modeling, dbt standards, warehouse architecture, and AI-readiness.
Key responsibilities include:
• Design and maintain high-quality data models, marts, and pipelines that enable fast, reliable, and reusable analysis across the organization.
• Own warehouse architecture, environments, permissions, performance tuning, cost management, data lifecycle, and operational hygiene to ensure the platform scales cleanly.
• Make the warehouse AI-readable by owning documentation, semantic context, metadata, lineage, and retrieval patterns that AI systems depend on to query data correctly.
• Define and champion dbt patterns, dimensional modeling practices, naming conventions, tests, and code review processes company-wide.
• Lead data governance practices, establishing standards for access, ownership, lineage, documentation, retention, quality, and sensitive data handling.
• Build with security and privacy in mind, partnering with engineering, security, legal, and finance to ensure appropriate data access and AI-enabled workflows.
• Automate data quality and maintenance through AI-assisted workflows that detect issues, explain root causes, suggest fixes, and reduce manual firefighting.
• Improve data team productivity by automating repetitive workflows, enhancing tooling, and streamlining development.
• Partner across the company with data scientists, engineering, product, finance, and GTM teams to translate analytical needs into durable data systems.
• Evaluate build-versus-buy tradeoffs, manage vendor relationships, and choose tools that scale with the team.
Requirements:
• 6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role.
• Deep SQL expertise—ability to reason about correctness, performance, joins, grain, and edge cases in complex warehouse queries.
• Strong data modeling experience with hands-on production dbt (or similar transformation framework) work; understanding of dimensional modeling, data contracts, testing, and schema evolution.
• Pipeline ownership—experience 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 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 focused on reliability, governance, security, cost, and long-term maintainability.
Bonus qualifications: Snowflake administration, optimization, cost management, or performance tuning; RBAC, PII handling, data classification, retention policies, audit workflows, or privacy/security reviews; Databricks or other modern data infrastructure; semantic layers, metrics layers, metadata systems, or data catalogs; Python for data tooling, automation, orchestration, or quality checks; early-stage analytics/data engineering experience at high-growth startups.
