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Perplexity is looking for experienced Data Platform Engineers to design, build, and scale the foundational data systems that power our product, AI research, analytics, and decision-making at scale. In this role, you will develop and own critical infrastructure for batch and streaming data processing, data orchestration, reliability, and developer experience across the data stack. You’ll work closely with engineering and data science teams to ensure data is accurate, timely, discoverable, and trustworthy, while enabling teams to move fast without sacrificing correctness or scale. This is a high-impact, senior/staff-level role where you will shape architecture, set standards, and drive long-term technical direction for Perplexity’s data ecosystem.
Job Responsibility:
Design and operate large-scale batch and streaming data pipelines supporting product features, AI training/evaluation, analytics, and experimentation
Build and evolve event-driven and streaming systems (e.g., Kafka/Kinesis/PubSub-style architectures) for real-time ingestion, transformation, and delivery
Own batch processing frameworks for backfills, aggregations, and offline computation
Lead the design and operation of data orchestration systems (e.g., Airflow, Dagster, or equivalent), including scheduling, dependency management, retries, SLAs, and observability
Establish strong guarantees around data correctness, freshness, lineage, and recoverability
Design systems that handle scale, partial failure, and evolving schemas
Build self-serve data platforms that empower engineers, data scientists, and analysts to safely create and operate pipelines
Improve developer experience for data work through better abstractions, tooling, documentation, and paved paths
Set standards for data modeling, testing, validation, and deployment
Drive architectural decisions across data infrastructure for storage, compute, orchestration, and APIs
Partner closely with engineering and data science teams to align data systems with evolving requirements
Mentor engineers, review designs, and raise the technical bar across the organization
Requirements:
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: you understand tradeoffs across reliability, latency, cost, and complexity
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