Member of Data Staff (Analytics Engineer)

Навыки

• Data Governance
• Data Quality
• Databricks
• dbt
• DWH
• Google Tag Manager
• Room / Realm / CoreData

О компании и продукте

• Own the foundation - your work will determine how fast and confidently the company can use data.
• Build for humans and AI - the next generation of data infrastructure needs to be understandable by agents as well as analysts.
• High leverage - better models, pipelines, and tools multiply every data scientist and every stakeholder who depends on data.
• Small team, broad scope - you'll have room to define standards, choose tools, and ship systems that become company-wide defaults.

Задачи

• 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
• Perplexity is AI for people who expect more
• On the data team, that means building the systems that make our data reliable, understandable, and usable by both humans and AI
• 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

Требования

• 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
• 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

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