Member of Data Staff (Analytics Engineer)

Perplexity is AI for people who expect more. On the data team, that means building the systems that make our data reliable, understandable, and usable by both humans and AI. We're looking for an analytics engineer or data engineer who wants 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: helping teams make strategic decisions, operate the business, and move faster with trusted data. You'll also make sure those systems are secure, privacy-aware, and legible to AI agents, data scientists, and the rest of the company. This role is for someone who can operate at the boundary of analytics engineering, data engineering, data governance, and internal product. You care about dimensional modeling, dbt standards, cost-aware warehouse design, access controls, privacy, and the details that make data trustworthy. You also believe AI should make the data stack faster, easier to maintain, and more accessible across the company without weakening security or governance.

Responsibilities

Build the core data foundation by designing and maintaining high-quality data models, marts, and pipelines that make analysis fast, reliable, and reusable; Manage the data warehouse, including architecture, environments, permissions, performance, cost, data lifecycle, and operational hygiene; Make the warehouse AI-readable by owning documentation, semantic context, metadata, lineage, and retrieval patterns; Own data modeling standards by defining and championing dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes; Lead data governance practices by defining standards for access, ownership, lineage, documentation, retention, quality, and sensitive data handling; Build with security and privacy in mind by partnering with engineering, security, legal, and finance to keep data access, sharing, and AI-enabled workflows appropriate and controlled; Automate data quality and maintenance by building AI-assisted workflows that detect issues, explain root causes, suggest fixes, generate tests, and reduce manual firefighting; Improve data team productivity by automating repetitive workflows, improving tooling, and making it easier for data scientists and stakeholders to answer questions; Partner across the company with data scientists, engineering, product, finance, and GTM teams to translate analytical needs into durable data systems; Shape tooling decisions by evaluating build-versus-buy tradeoffs, managing vendor relationships when needed, and choosing 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, including reasoning 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, dimensional modeling, data contracts, testing, and schema evolution; Pipeline ownership, including building, maintaining, debugging, and improving production data pipelines; Warehouse management experience, including warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership; Governance mindset, including data ownership, access controls, privacy, retention, lineage, auditability, and the risks of making data too easy to access; AI-native working style, using AI to speed up development, documentation, QA, exploration, and repetitive workflow automation; Stakeholder fluency, turning messy analytical requirements into trusted models, metrics, and reusable data assets; Autonomy and execution, taking projects from ambiguous problem to production-quality system with minimal oversight; Operational judgment, caring about reliability, governance, security, cost, and long-term maintainability

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/security reviews; Experience with 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; Previous experience as an early analytics engineer or data engineer at a high-growth startup