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# Member of Data Staff (Analytics Engineer)

- **Company:** PERPLEXITY LLC
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $175,000.0 - $330,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Data Infrastructure, Data Security, Data Systems, Data Warehousing, Dimensional Modeling, Python (Programming Language), Metadata, Metadata Repositories, Operational Databases, Performance Tuning, Role-Based Access Control, Standard Sql, Perplexity AI, Data Processing, Data Classification, Snowflake, Data Layers, Core Data, Databricks
- **Published:** October 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=dd3b359531e77719

## About the Role

* 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.

## Description

* 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., * 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.