• 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.

• 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.

Bonus

• 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.

Why This Role

• 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.