Member of Data Staff (Analytics Engineer)

San Francisco · New York City · 6+ years experience · Full-time · Hybrid · Posted Oct 5, 2026

At a glance

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 position operates at the boundary of analytics engineering, data engineering, data governance, and internal product.

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 by helping own warehouse 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 that AI systems depend on.
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 ensure data access and AI-enabled workflows are controlled.
Automate data quality and maintenance by building AI-assisted workflows that detect issues, explain root causes, suggest fixes, and generate tests.
Improve data team productivity by automating repetitive workflows, improving tooling, and streamlining development for data scientists and stakeholders.
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, and choosing tools that scale with the team.

6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role.
Deep SQL expertise with the ability to reason about correctness, performance, joins, grain, and edge cases in complex warehouse queries.
Strong data modeling experience with hands-on work in dbt or a similar transformation framework in production, including dimensional modeling and data contracts.
Pipeline ownership experience building, maintaining, debugging, and improving production data pipelines.
Warehouse management experience with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership.
Governance mindset focused on data ownership, access controls, privacy, retention, lineage, and auditability.
AI-native working style utilizing AI to speed up development, documentation, QA, exploration, and repetitive workflow automation.
Stakeholder fluency in turning messy analytical requirements into trusted models, metrics, and reusable data assets.
Autonomy and execution capabilities to take projects from ambiguous problems to production-quality systems with minimal oversight.
Operational judgment emphasizing reliability, governance, security, cost, and long-term maintainability.

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.