Data & AnalyticsOn-site

Member of Data Staff (Analytics Engineer)

Perplexity · On-site

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Last seen by MeritLog September 10, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from Perplexity's Ashby job board and last checked it on September 10, 2026.

Source: the employer's Ashby job board. Open the original listing for current details.

Job details

Work model: On-site
Salary: $175K - $330K
Location: San Francisco

Hiring context

How this role compares at Perplexity

Perplexity has 113 live roles in MeritLog’s catalog across 11 job families, and 21 of them are in data & analytics. 97 of those listings publish a pay range, a disclosure rate of 86%.

This role's posted range of $175K - $330K sits above 50% of the 96 other Perplexity roles quoted over the same currency and period.

Perplexity concentrates this hiring in:

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What you'd do

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.

What they're asking for

Snowflake administration, optimization, cost management, or warehouse performance tuning.SkillPreferred
Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy/security reviews.SkillPreferred
Experience with Databricks or other modern data infrastructure.SkillPreferred
Experience building semantic layers, metrics layers, metadata systems, or data catalogs.SkillPreferred
Python experience for data tooling, automation, orchestration, or quality checks.SkillPreferred
Previous experience as an early analytics engineer or data engineer at a high-growth startup.SkillPreferred
Own the foundation - your work will determine how fast and confidently the company can use data.SkillPreferred
Build for humans and AI - the next generation of data infrastructure needs to be understandable by agents as well as analysts.SkillPreferred
High leverage - better models, pipelines, and tools multiply every data scientist and every stakeholder who depends on data.SkillPreferred
Small team, broad scope - you'll have room to define standards, choose tools, and ship systems that become company-wide defaults.SkillPreferred

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job 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 DO - 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. WHAT WE'RE LOOKING FOR - 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.

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