Member of Data Staff (Analytics Engineer)

Perplexity•San Francisco, CA

4d•$175,000 - $330,000

About The Position

Perplexity is looking for an analytics engineer or data engineer to build the foundation for an AI-native data organization. This role involves designing core data models, pipelines, semantic layers, data quality systems, governance practices, and warehouse workflows that power the entire company. The goal is to help teams make strategic decisions, operate the business, and move faster with trusted data, while ensuring systems are secure, privacy-aware, and legible to AI agents, data scientists, and the rest of the company. This position operates at the intersection of analytics engineering, data engineering, data governance, and internal product, with a focus on dimensional modeling, dbt standards, cost-aware warehouse design, access controls, privacy, and data trustworthiness. The ideal candidate believes AI can enhance the data stack's speed, ease of maintenance, and accessibility without compromising security or governance.

Requirements

6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role.
Deep SQL expertise - you can reason about correctness, performance, joins, grain, and edge cases in complex warehouse queries.
Strong data modeling experience - you've worked hands-on with dbt (or a similar transformation framework) in production, and you understand dimensional modeling, data contracts, testing, and how analytical schemas should evolve.
Pipeline ownership - you've built, maintained, debugged, and improved production data pipelines.
Warehouse management experience - you've worked with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership.
Governance mindset - you think clearly about data ownership, access controls, privacy, retention, lineage, auditability, and the risks of making data too easy to access.
AI-native working style - you already use AI to speed up development, documentation, QA, exploration, and repetitive workflow automation.
Stakeholder fluency - you know how to turn messy analytical requirements into trusted models, metrics, and reusable data assets.
Autonomy and execution - you can take projects from ambiguous problem to production-quality system with minimal oversight.
Operational judgment - you care about reliability, governance, security, cost, and long-term maintainability.

Nice To Haves

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.

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 so the platform scales cleanly.
Make the warehouse AI-readable - own the documentation, semantic context, metadata, lineage, and retrieval patterns that AI systems depend on to understand and query Perplexity's data correctly.
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 across the analytical warehouse.
Build with security and privacy in mind - partner with engineering, security, legal, and finance where needed to ensure data access, sharing, and AI-enabled workflows are appropriate and controlled.
Automate data quality and maintenance - build AI-assisted workflows that detect issues, explain root causes, suggest fixes, generate tests, and reduce manual firefighting.
Improve data team productivity - automate repetitive workflows, improve tooling, streamline development, and make it easier for data scientists and stakeholders to answer questions.
Partner across the company - work closely with data scientists, engineering, product, finance, and GTM teams to translate analytical needs into durable data systems.
Shape tooling decisions - evaluate build-versus-buy tradeoffs, manage vendor relationships when needed, and choose tools that scale with the team.