Member of Data Staff (Analytics Engineer)

Perplexity AI; États-Unis; Sur place; USD 150 000 - 210 000; Plein temps

Résumé du poste

Perplexity AI is seeking an analytics engineer or data engineer to build the foundation of an AI-native data organization. You will design core data models, pipelines, semantic layers, and governance practices that power the company’s analytics and AI agents, while ensuring privacy and security.

You will own warehouse architecture, performance, and scalable data contracts, collaborating with data scientists, engineering, and product teams to enable fast, trustworthy analyses across Perplexity’s

Qualifications

• 6+ years of experience in analytics engineering, data engineering, data science, or related roles.
• Deep SQL expertise with complex warehouse queries, joins, grain, and edge cases.
• Hands-on dbt experience in production; dimensional modeling, data contracts, testing.
• Strong ability to translate messy requirements into trusted models and metrics.
• Experience with warehouse administration, permissions, performance tuning, and cost management.
• Governance mindset covering ownership, lineage, retention, privacy, auditability.

Responsabilités

• Own and optimize core data pipelines and warehouse workflows for speed and reliability.
• Define and enforce data modeling standards, naming conventions, tests, and reviews.
• Lead data governance practices across the analytical warehouse with stakeholders.
• Build AI-assisted workflows for QA, documentation, and automation; reduce manual firefighting.

Connaissances

SQL excellence

Data modeling

dbt patterns

Stakeholder communication

Security and privacy mindset

AI-assisted development

Autonomy and execution

Outils

Databricks

Snowflake

Python

Metadata/semantics tooling

Description du poste

• 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
• 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

• Pipeline ownership - you’ve built, maintained, debugged, and improved production data pipelines
• Autonomy and execution - you can take projects from ambiguous problem to production-quality system with minimal oversight
• 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
• Stakeholder fluency - you know how to turn messy analytical requirements into trusted models, metrics, and reusable data assets
• 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
• Operational judgment - you care about reliability, governance, security, cost, and long-term maintainability
• AI-native working style - you already use AI to speed up development, documentation, QA, exploration, and repetitive workflow automation
• Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy/security reviews
• Experience with Databricks or other modern data infrastructure
• Snowflake administration, optimization, cost management, or warehouse performance tuning
• 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
• Experience building semantic layers, metrics layers, metadata systems, or data catalogs