Member of Technical Staff (Software Engineer, Data Platform)

About the role

The Data Platform team owns the end-to-end data lifecycle at Perplexity, from ingestion through processing, storage, and serving, powering product features, analytics, experimentation, AI workloads, and the company’s data lake.

What you’ll do

• Design and operate large-scale batch and streaming data pipelines that directly power Perplexity product features, AI training and evaluation workflows, analytics, and experimentation.
• Build event-driven and streaming systems (Kafka, Kinesis, PubSub, or similar) for real-time ingestion, transformation, and delivery, alongside batch frameworks for backfills, aggregations, and offline computation.
• Lead the architecture of data orchestration using tools like Airflow or Dagster, owning scheduling, dependency management, retries, SLAs, and end-to-end observability for critical data flows.
• Set and enforce guarantees for data correctness, freshness, lineage, and recoverability, designing systems that handle rapid scale growth, partial failures, and evolving schemas without disrupting AI workloads or product experiences.
• Build self-serve data platforms that let engineers, data scientists, and analysts safely discover data, define contracts, and create and operate their own pipelines with minimal friction.
• Improve developer experience through better abstractions, opinionated paved paths, and standards for data modeling, testing, validation, and deployment, treating the data platform as a product used by many teams.
• Drive architectural decisions across storage, compute, orchestration, and data APIs, partnering closely with product engineering and data science to align the data ecosystem with Perplexity’s roadmap.
• Mentor engineers, review designs, and raise the technical bar for data infrastructure through thoughtful feedback, documentation, and hands-on collaboration.

What they’re looking for

Select a requirement to inspect fit, evidence or application context.

• 5+ years (Senior) or 8+ years (Staff) of software engineering experience.
• Strong experience building production data infrastructure systems.
• Hands-on experience with batch and/or streaming data processing at scale.
• Deep familiarity with data orchestration systems (Airflow, Dagster, or similar).
• Proficiency in Python and at least one additional backend language (Go, TypeScript, etc.).
• Strong systems thinking around reliability, latency, cost, and complexity tradeoffs.
• Experience supporting ML/AI workflows, training pipelines, or evaluation systems.
• Familiarity with data quality, lineage, observability, and governance tooling.
• Prior ownership of internal platforms used by many teams.