# Member of Data Staff (Analytics Engineer)

**Company**: Perplexity
**Location**: San Francisco
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: Full time
**Salary**: $175K - $330K
**Category**: Engineering
**Industry**: Technology

**Apply**: https://jobs.ashbyhq.com/perplexity/71abd198-5a69-4837-aa59-df8eeb9e02eb?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_13658a9b-cf1

## Description

We are looking for an analytics engineer or data engineer who wants to build the foundation for an AI-native data organization. You will 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.

**Responsibilities**

- 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.

**Requirements**

- 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.

**Nice to Have**

- 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.

**Compensation**

- $175K – $330K • Offers Equity

## Skills

### Required
- SQL
- data modeling
- dbt
- warehouse management
- governance

### Nice to have
- Snowflake
- Databricks
- Python
- RBAC
- PII handling

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Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/perplexity/71abd198-5a69-4837-aa59-df8eeb9e02eb?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)