layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Member of Data Staff (Analytics Engineer) at Perplexity

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.

Member of Data Staff (Analytics Engineer)

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