Member of Data Staff (Analytics Engineer)
Posted Updated
Summary
Analytics engineer at Perplexity AI building the company's data foundation: designing dbt data models and pipelines, managing the warehouse (Snowflake/Databricks), and creating AI-readable semantic layers and governance so teams can trust and query data. Requires 6+ years with deep SQL, dbt, and warehouse administration.
- 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
