At a glance
Build Perplexity’s trusted data foundation: models, pipelines, warehouse governance, semantic context, and AI-assisted quality workflows that help humans and AI agents use company data safely and effectively.
Summarized by AI from the original posting
What you'll do
- Build and maintain data models, marts, and pipelines
- Manage warehouse architecture, permissions, performance, cost, and lifecycle
- Make warehouse data understandable and queryable by AI systems
- Define dbt, dimensional modeling, testing, and naming standards
- Lead data governance practices
- Build secure and privacy-aware data workflows
- Automate data quality and maintenance with AI-assisted workflows
- Improve data team productivity and tooling
- Partner with data scientists, engineering, product, finance, and GTM
- Evaluate tooling and build-versus-buy decisions
Requirements
- 01Deep SQL expertise
- 02Production experience with dbt or similar transformation frameworks
- 03Experience building and maintaining production data pipelines
- 04Warehouse administration and performance tuning experience
- 05Data governance and access control expertise
- 06Autonomous project execution
- 07Operational judgment
Perks
Skills
Full description
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.
What You'll DoBuild 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.
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.
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.