Member of Data Staff (Analytics Engineer)
San Francisco • FullTime
Posted 6mo 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 the entire company. You will ensure these systems are secure, privacy-aware, and accessible to AI agents, data scientists, and other stakeholders, enabling strategic decision-making and efficient operations with trusted data. The ideal candidate operates at the intersection of analytics engineering, data engineering, data governance, and internal product development, with a strong focus on dimensional modeling, dbt standards, cost-aware warehouse design, and data trustworthiness, while believing AI can enhance the data stack's speed, maintainability, and accessibility.
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 with security and privacy in mind, ensuring appropriate and controlled data access, sharing, and AI-enabled workflows.
- Automate data quality and maintenance using AI-assisted workflows to detect issues, explain root causes, and suggest fixes.
- Improve data team productivity by automating repetitive workflows, enhancing tooling, and streamlining development.
- Partner with cross-functional teams to translate analytical needs into durable data systems.
- Evaluate build-versus-buy tradeoffs and manage vendor relationships for tooling decisions.
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 queries.
- Strong data modeling experience with dbt (or similar) in production, including dimensional modeling, data contracts, testing, and schema evolution.
- Experience building, maintaining, debugging, and improving production data pipelines.
- Warehouse management experience, including administration, access patterns, permissions, performance tuning, cost management, or operational ownership.
- A governance mindset, with clear thinking about data ownership, access controls, privacy, retention, lineage, and auditability.
- An AI-native working style, utilizing AI for development, documentation, QA, exploration, and workflow automation.
- Fluency in translating ambiguous analytical requirements into trusted models, metrics, and reusable data assets.
- Ability to take projects from ambiguous problems to production-quality systems with minimal oversight.
- Strong operational judgment, prioritizing reliability, governance, security, cost, and long-term maintainability.
Benefits
- Own the foundation of how the company uses data.
- Build for humans and AI.
- High leverage role multiplying the impact of data scientists and stakeholders.
- Small team with broad scope to define standards and choose tools.