Member of Data Staff (Analytics Engineer)

USD 175,000-330,000 per year
MIDDLE
✅ Hybrid

Tech Stack

AI @ 3 Audit @ 3 Data Engineering @ 6 Data Pipelines @ 3 Databricks @ 3 Debugging @ 3 Python @ 3 SQL @ 3 Security @ 6 Snowflake @ 3 dbt @ 6

Details

Perplexity is building an AI-native data organization. This role will establish the systems that make data reliable, understandable, secure, privacy-aware, and usable by humans and AI systems. The position operates at the intersection of analytics engineering, data engineering, data governance, and internal product development.

Responsibilities

  • Design and maintain high-quality data models, marts, and pipelines.
  • Help own warehouse architecture, environments, permissions, performance, cost management, data lifecycle, and operational hygiene.
  • Build documentation, semantic context, metadata, lineage, and retrieval patterns that enable AI systems to understand and query company data correctly.
  • Define and promote dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes.
  • Establish standards for data access, ownership, lineage, documentation, retention, quality, and sensitive data handling.
  • Partner with engineering, security, legal, and finance to ensure data access, sharing, and AI-enabled workflows are appropriately controlled.
  • Build AI-assisted data quality workflows that detect issues, explain root causes, suggest fixes, generate tests, and reduce manual firefighting.
  • Automate repetitive workflows, improve tooling, streamline development, and help data scientists and stakeholders answer questions.
  • Partner with data scientists, engineering, product, finance, and go-to-market teams to translate analytical needs into durable data systems.
  • Evaluate build-versus-buy tradeoffs, manage vendor relationships when needed, and select scalable tools.

Requirements

  • 6+ years of experience as an analytics engineer, data engineer, data scientist, or in a closely related role.
  • Deep SQL expertise, including correctness, performance, joins, grain, and edge cases in complex warehouse queries.
  • Strong hands-on production experience with dbt or a similar transformation framework.
  • Understanding of dimensional modeling, data contracts, testing, and evolving analytical schemas.
  • Experience building, maintaining, debugging, and improving production data pipelines.
  • Experience with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership.
  • Understanding of data ownership, access controls, privacy, retention, lineage, auditability, and data-access risks.
  • An AI-native working style, including use of AI for development, documentation, quality assurance, exploration, and workflow automation.
  • Ability to translate ambiguous analytical requirements into trusted models, metrics, and reusable data assets.
  • Ability to take projects from ambiguous problem to production-quality system with minimal oversight.
  • Strong judgment regarding reliability, governance, security, cost, and long-term maintainability.

Bonus Qualifications

  • Snowflake administration, optimization, cost management, or warehouse performance tuning.
  • Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy and 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.
  • Experience as an early analytics engineer or data engineer at a high-growth startup.

Role Highlights

  • Own the data foundation that determines how quickly and confidently the company can use data.
  • Build data infrastructure that is understandable by both AI agents and analysts.
  • Create high-leverage models, pipelines, and tools for data scientists and stakeholders.
  • Work on a small team with broad scope to define standards, choose tools, and ship company-wide systems.

Benefits

Full-time U.S. employees receive benefits including equity, health, dental, vision, retirement, fitness, commuter and dependent care accounts, and more. International employees receive benefits tailored to their region of residence.

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