Member of Data Staff (Analytics Engineer)
Remote • San Francisco • FullTime
Posted 8h ago
About the job
Perplexity is seeking an Analytics Engineer or Data Engineer to build the foundational systems for an AI-native data organization. This role involves designing and maintaining core data models, pipelines, semantic layers, data quality systems, and governance practices that empower company-wide decision-making and operations. You will ensure these systems are secure, privacy-aware, and accessible to AI agents, data scientists, and other stakeholders, operating at the intersection of analytics engineering, data engineering, data governance, and internal product development. The ideal candidate is passionate about dimensional modeling, dbt standards, cost-efficient warehouse design, access controls, and data trustworthiness, believing that AI should enhance the data stack's speed, maintainability, and accessibility without compromising security or governance.
Responsibilities
- Design and maintain high-quality data models, marts, and pipelines for fast, reliable, and reusable analysis.
- Manage data warehouse architecture, environments, permissions, performance, cost, data lifecycle, and operational hygiene.
- Make the data warehouse AI-readable by owning documentation, semantic context, metadata, lineage, and retrieval patterns.
- Define and champion dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes.
- Define standards for access, ownership, lineage, documentation, retention, quality, and sensitive data handling.
- Build data systems with security and privacy in mind, partnering with relevant teams.
- Develop AI-assisted workflows to automate data quality checks, issue detection, root cause analysis, and maintenance.
- Improve data team productivity by automating repetitive workflows, enhancing tooling, and streamlining development.
- Collaborate with data scientists, engineering, product, finance, and GTM teams to translate analytical needs into durable data systems.
- Evaluate build-versus-buy tradeoffs and manage vendor relationships for data tooling.
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.
- Strong data modeling experience with dbt (or similar), including dimensional modeling, data contracts, testing, and schema evolution.
- Proven experience building, maintaining, debugging, and improving production data pipelines.
- Experience with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership.
- A governance mindset, considering data ownership, access controls, privacy, retention, lineage, and auditability.
- An AI-native working style, utilizing AI for development, documentation, QA, exploration, and automation.
- Fluency in translating messy analytical requirements into trusted models, metrics, and reusable data assets.
- Ability to take projects from ambiguity to production-quality systems with minimal oversight.
- Strong operational judgment regarding reliability, governance, security, cost, and long-term maintainability.